| --- |
| tags: |
| - reinforcement-learning |
| - atari |
| - babyai |
| - metaworld |
| - mujoco-ant |
| - mujoco |
| datasets: jat-project/jat-dataset |
| pipeline_tag: reinforcement-learning |
| model-index: |
| - name: jat-project/jat |
| results: |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Atari 57 |
| type: atari |
| metrics: |
| - type: iqm_expert_normalized_total_reward |
| value: 0.14 [0.14, 0.15] |
| name: IQM expert normalized total reward |
| - type: iqm_human_normalized_total_reward |
| value: 0.38 [0.37, 0.39] |
| name: IQM human normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: BabyAI |
| type: babyai |
| metrics: |
| - type: iqm_expert_normalized_total_reward |
| value: 0.99 [0.99, 0.99] |
| name: IQM expert normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: MetaWorld |
| type: metaworld |
| metrics: |
| - type: iqm_expert_normalized_total_reward |
| value: 0.65 [0.64, 0.67] |
| name: IQM expert normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: MuJoCo |
| type: mujoco |
| metrics: |
| - type: iqm_expert_normalized_total_reward |
| value: 0.85 [0.83, 0.86] |
| name: IQM expert normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Alien |
| type: atari-alien |
| metrics: |
| - type: total_reward |
| value: 1518.70 +/- 568.14 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 0.08 +/- 0.03 |
| name: Expert normalized total reward |
| - type: human_normalized_total_reward |
| value: 0.19 +/- 0.08 |
| name: Human normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Amidar |
| type: atari-amidar |
| metrics: |
| - type: total_reward |
| value: 89.17 +/- 78.73 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 0.04 +/- 0.04 |
| name: Expert normalized total reward |
| - type: human_normalized_total_reward |
| value: 0.05 +/- 0.05 |
| name: Human normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Assault |
| type: atari-assault |
| metrics: |
| - type: total_reward |
| value: 1676.91 +/- 780.73 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 0.09 +/- 0.05 |
| name: Expert normalized total reward |
| - type: human_normalized_total_reward |
| value: 2.80 +/- 1.50 |
| name: Human normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Asterix |
| type: atari-asterix |
| metrics: |
| - type: total_reward |
| value: 844.50 +/- 546.85 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 0.18 +/- 0.16 |
| name: Expert normalized total reward |
| - type: human_normalized_total_reward |
| value: 0.08 +/- 0.07 |
| name: Human normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Asteroids |
| type: atari-asteroids |
| metrics: |
| - type: total_reward |
| value: 1357.90 +/- 453.01 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 0.00 +/- 0.00 |
| name: Expert normalized total reward |
| - type: human_normalized_total_reward |
| value: 0.01 +/- 0.01 |
| name: Human normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Atlantis |
| type: atari-atlantis |
| metrics: |
| - type: total_reward |
| value: 51843.00 +/- 123857.07 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 0.13 +/- 0.40 |
| name: Expert normalized total reward |
| - type: human_normalized_total_reward |
| value: 2.41 +/- 7.66 |
| name: Human normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Bank Heist |
| type: atari-bankheist |
| metrics: |
| - type: total_reward |
| value: 977.80 +/- 156.49 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 0.74 +/- 0.12 |
| name: Expert normalized total reward |
| - type: human_normalized_total_reward |
| value: 1.30 +/- 0.21 |
| name: Human normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Battle Zone |
| type: atari-battlezone |
| metrics: |
| - type: total_reward |
| value: 16780.00 +/- 6926.15 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 0.06 +/- 0.02 |
| name: Expert normalized total reward |
| - type: human_normalized_total_reward |
| value: 0.45 +/- 0.19 |
| name: Human normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Beam Rider |
| type: atari-beamrider |
| metrics: |
| - type: total_reward |
| value: 768.36 +/- 364.06 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 0.01 +/- 0.01 |
| name: Expert normalized total reward |
| - type: human_normalized_total_reward |
| value: 0.02 +/- 0.02 |
| name: Human normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Berzerk |
| type: atari-berzerk |
| metrics: |
| - type: total_reward |
| value: 616.20 +/- 296.08 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 0.01 +/- 0.01 |
| name: Expert normalized total reward |
| - type: human_normalized_total_reward |
| value: 0.20 +/- 0.12 |
| name: Human normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Bowling |
| type: atari-bowling |
| metrics: |
| - type: total_reward |
| value: 22.32 +/- 5.18 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 1.00 +/- 0.00 |
| name: Expert normalized total reward |
| - type: human_normalized_total_reward |
| value: -0.01 +/- 0.04 |
| name: Human normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Boxing |
| type: atari-boxing |
| metrics: |
| - type: total_reward |
| value: 92.31 +/- 18.24 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 0.94 +/- 0.19 |
| name: Expert normalized total reward |
| - type: human_normalized_total_reward |
| value: 7.68 +/- 1.52 |
| name: Human normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Breakout |
| type: atari-breakout |
| metrics: |
| - type: total_reward |
| value: 7.93 +/- 5.66 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 0.01 +/- 0.01 |
| name: Expert normalized total reward |
| - type: human_normalized_total_reward |
| value: 0.22 +/- 0.20 |
| name: Human normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Centipede |
| type: atari-centipede |
| metrics: |
| - type: total_reward |
| value: 5888.27 +/- 2594.62 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 0.40 +/- 0.27 |
| name: Expert normalized total reward |
| - type: human_normalized_total_reward |
| value: 0.38 +/- 0.26 |
| name: Human normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Chopper Command |
| type: atari-choppercommand |
| metrics: |
| - type: total_reward |
| value: 2371.00 +/- 1195.43 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 0.02 +/- 0.01 |
| name: Expert normalized total reward |
| - type: human_normalized_total_reward |
| value: 0.24 +/- 0.18 |
| name: Human normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Crazy Climber |
| type: atari-crazyclimber |
| metrics: |
| - type: total_reward |
| value: 97145.00 +/- 30388.04 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 0.51 +/- 0.18 |
| name: Expert normalized total reward |
| - type: human_normalized_total_reward |
| value: 3.45 +/- 1.21 |
| name: Human normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Defender |
| type: atari-defender |
| metrics: |
| - type: total_reward |
| value: 39317.50 +/- 16246.15 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 0.10 +/- 0.05 |
| name: Expert normalized total reward |
| - type: human_normalized_total_reward |
| value: 2.30 +/- 1.03 |
| name: Human normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Demon Attack |
| type: atari-demonattack |
| metrics: |
| - type: total_reward |
| value: 795.10 +/- 982.55 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 0.01 +/- 0.01 |
| name: Expert normalized total reward |
| - type: human_normalized_total_reward |
| value: 0.35 +/- 0.54 |
| name: Human normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Double Dunk |
| type: atari-doubledunk |
| metrics: |
| - type: total_reward |
| value: 13.40 +/- 11.07 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 0.81 +/- 0.28 |
| name: Expert normalized total reward |
| - type: human_normalized_total_reward |
| value: 0.91 +/- 0.32 |
| name: Human normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Enduro |
| type: atari-enduro |
| metrics: |
| - type: total_reward |
| value: 103.11 +/- 28.05 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 0.04 +/- 0.01 |
| name: Expert normalized total reward |
| - type: human_normalized_total_reward |
| value: 0.12 +/- 0.03 |
| name: Human normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Fishing Derby |
| type: atari-fishingderby |
| metrics: |
| - type: total_reward |
| value: -31.67 +/- 22.54 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 0.61 +/- 0.23 |
| name: Expert normalized total reward |
| - type: human_normalized_total_reward |
| value: 0.46 +/- 0.17 |
| name: Human normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Freeway |
| type: atari-freeway |
| metrics: |
| - type: total_reward |
| value: 27.57 +/- 1.87 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 0.81 +/- 0.06 |
| name: Expert normalized total reward |
| - type: human_normalized_total_reward |
| value: 0.93 +/- 0.06 |
| name: Human normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Frostbite |
| type: atari-frostbite |
| metrics: |
| - type: total_reward |
| value: 2875.60 +/- 1679.84 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 0.21 +/- 0.13 |
| name: Expert normalized total reward |
| - type: human_normalized_total_reward |
| value: 0.66 +/- 0.39 |
| name: Human normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Gopher |
| type: atari-gopher |
| metrics: |
| - type: total_reward |
| value: 5508.80 +/- 2802.03 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 0.06 +/- 0.03 |
| name: Expert normalized total reward |
| - type: human_normalized_total_reward |
| value: 2.44 +/- 1.30 |
| name: Human normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Gravitar |
| type: atari-gravitar |
| metrics: |
| - type: total_reward |
| value: 1330.50 +/- 918.23 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 0.30 +/- 0.24 |
| name: Expert normalized total reward |
| - type: human_normalized_total_reward |
| value: 0.36 +/- 0.29 |
| name: Human normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: H.E.R.O. |
| type: atari-hero |
| metrics: |
| - type: total_reward |
| value: 11932.00 +/- 3036.87 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 0.25 +/- 0.07 |
| name: Expert normalized total reward |
| - type: human_normalized_total_reward |
| value: 0.37 +/- 0.10 |
| name: Human normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Ice Hockey |
| type: atari-icehockey |
| metrics: |
| - type: total_reward |
| value: 7.61 +/- 5.28 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 0.52 +/- 0.15 |
| name: Expert normalized total reward |
| - type: human_normalized_total_reward |
| value: 1.55 +/- 0.44 |
| name: Human normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: James Bond |
| type: atari-jamesbond |
| metrics: |
| - type: total_reward |
| value: 425.00 +/- 632.51 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 0.01 +/- 0.02 |
| name: Expert normalized total reward |
| - type: human_normalized_total_reward |
| value: 1.45 +/- 2.31 |
| name: Human normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Kangaroo |
| type: atari-kangaroo |
| metrics: |
| - type: total_reward |
| value: 375.00 +/- 314.13 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 0.62 +/- 0.60 |
| name: Expert normalized total reward |
| - type: human_normalized_total_reward |
| value: 0.11 +/- 0.11 |
| name: Human normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Krull |
| type: atari-krull |
| metrics: |
| - type: total_reward |
| value: 10743.30 +/- 1311.26 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 0.93 +/- 0.13 |
| name: Expert normalized total reward |
| - type: human_normalized_total_reward |
| value: 8.57 +/- 1.23 |
| name: Human normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Kung-Fu Master |
| type: atari-kungfumaster |
| metrics: |
| - type: total_reward |
| value: 253.00 +/- 233.86 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: -0.00 +/- 0.01 |
| name: Expert normalized total reward |
| - type: human_normalized_total_reward |
| value: -0.00 +/- 0.01 |
| name: Human normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Montezuma's Revenge |
| type: atari-montezumarevenge |
| metrics: |
| - type: total_reward |
| value: 0.00 +/- 0.00 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 0.00 +/- 0.00 |
| name: Expert normalized total reward |
| - type: human_normalized_total_reward |
| value: 0.00 +/- 0.00 |
| name: Human normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Ms. Pacman |
| type: atari-mspacman |
| metrics: |
| - type: total_reward |
| value: 1610.10 +/- 504.08 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 0.20 +/- 0.08 |
| name: Expert normalized total reward |
| - type: human_normalized_total_reward |
| value: 0.20 +/- 0.08 |
| name: Human normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Name This Game |
| type: atari-namethisgame |
| metrics: |
| - type: total_reward |
| value: 7726.40 +/- 2166.18 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 0.26 +/- 0.10 |
| name: Expert normalized total reward |
| - type: human_normalized_total_reward |
| value: 0.94 +/- 0.38 |
| name: Human normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Phoenix |
| type: atari-phoenix |
| metrics: |
| - type: total_reward |
| value: 1814.20 +/- 1275.29 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 0.00 +/- 0.00 |
| name: Expert normalized total reward |
| - type: human_normalized_total_reward |
| value: 0.16 +/- 0.20 |
| name: Human normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: PitFall |
| type: atari-pitfall |
| metrics: |
| - type: total_reward |
| value: -4.61 +/- 15.86 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 0.99 +/- 0.07 |
| name: Expert normalized total reward |
| - type: human_normalized_total_reward |
| value: 0.03 +/- 0.00 |
| name: Human normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Pong |
| type: atari-pong |
| metrics: |
| - type: total_reward |
| value: 16.54 +/- 10.34 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 0.89 +/- 0.25 |
| name: Expert normalized total reward |
| - type: human_normalized_total_reward |
| value: 1.05 +/- 0.29 |
| name: Human normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Private Eye |
| type: atari-privateeye |
| metrics: |
| - type: total_reward |
| value: 44.00 +/- 49.64 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 0.25 +/- 0.66 |
| name: Expert normalized total reward |
| - type: human_normalized_total_reward |
| value: 0.00 +/- 0.00 |
| name: Human normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Q*Bert |
| type: atari-qbert |
| metrics: |
| - type: total_reward |
| value: 2118.50 +/- 2764.25 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 0.05 +/- 0.06 |
| name: Expert normalized total reward |
| - type: human_normalized_total_reward |
| value: 0.15 +/- 0.21 |
| name: Human normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: River Raid |
| type: atari-riverraid |
| metrics: |
| - type: total_reward |
| value: 3925.20 +/- 1530.94 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 0.19 +/- 0.11 |
| name: Expert normalized total reward |
| - type: human_normalized_total_reward |
| value: 0.16 +/- 0.10 |
| name: Human normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Road Runner |
| type: atari-roadrunner |
| metrics: |
| - type: total_reward |
| value: 6929.00 +/- 5577.35 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 0.09 +/- 0.07 |
| name: Expert normalized total reward |
| - type: human_normalized_total_reward |
| value: 0.88 +/- 0.71 |
| name: Human normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Robotank |
| type: atari-robotank |
| metrics: |
| - type: total_reward |
| value: 10.22 +/- 4.71 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 0.10 +/- 0.06 |
| name: Expert normalized total reward |
| - type: human_normalized_total_reward |
| value: 0.83 +/- 0.49 |
| name: Human normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Seaquest |
| type: atari-seaquest |
| metrics: |
| - type: total_reward |
| value: 859.80 +/- 407.80 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 0.31 +/- 0.16 |
| name: Expert normalized total reward |
| - type: human_normalized_total_reward |
| value: 0.02 +/- 0.01 |
| name: Human normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Skiing |
| type: atari-skiing |
| metrics: |
| - type: total_reward |
| value: -15960.04 +/- 5887.52 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 0.18 +/- 0.93 |
| name: Expert normalized total reward |
| - type: human_normalized_total_reward |
| value: 0.09 +/- 0.46 |
| name: Human normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Solaris |
| type: atari-solaris |
| metrics: |
| - type: total_reward |
| value: 1202.60 +/- 445.27 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: -0.29 +/- 3.79 |
| name: Expert normalized total reward |
| - type: human_normalized_total_reward |
| value: -0.00 +/- 0.04 |
| name: Human normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Space Invaders |
| type: atari-spaceinvaders |
| metrics: |
| - type: total_reward |
| value: 326.85 +/- 141.89 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 0.01 +/- 0.00 |
| name: Expert normalized total reward |
| - type: human_normalized_total_reward |
| value: 0.12 +/- 0.09 |
| name: Human normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Star Gunner |
| type: atari-stargunner |
| metrics: |
| - type: total_reward |
| value: 5219.00 +/- 3544.03 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 0.01 +/- 0.01 |
| name: Expert normalized total reward |
| - type: human_normalized_total_reward |
| value: 0.48 +/- 0.37 |
| name: Human normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Surround |
| type: atari-surround |
| metrics: |
| - type: total_reward |
| value: 1.52 +/- 4.60 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 0.59 +/- 0.24 |
| name: Expert normalized total reward |
| - type: human_normalized_total_reward |
| value: 0.70 +/- 0.28 |
| name: Human normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Tennis |
| type: atari-tennis |
| metrics: |
| - type: total_reward |
| value: -12.80 +/- 3.70 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 0.32 +/- 0.11 |
| name: Expert normalized total reward |
| - type: human_normalized_total_reward |
| value: 0.34 +/- 0.12 |
| name: Human normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Time Pilot |
| type: atari-timepilot |
| metrics: |
| - type: total_reward |
| value: 11603.00 +/- 4323.25 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 0.12 +/- 0.07 |
| name: Expert normalized total reward |
| - type: human_normalized_total_reward |
| value: 4.84 +/- 2.60 |
| name: Human normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Tutankham |
| type: atari-tutankham |
| metrics: |
| - type: total_reward |
| value: 108.82 +/- 70.14 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 0.35 +/- 0.25 |
| name: Expert normalized total reward |
| - type: human_normalized_total_reward |
| value: 0.62 +/- 0.45 |
| name: Human normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Up and Down |
| type: atari-upndown |
| metrics: |
| - type: total_reward |
| value: 19074.60 +/- 9961.77 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 0.04 +/- 0.02 |
| name: Expert normalized total reward |
| - type: human_normalized_total_reward |
| value: 1.66 +/- 0.89 |
| name: Human normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Venture |
| type: atari-venture |
| metrics: |
| - type: total_reward |
| value: 0.00 +/- 0.00 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 1.00 +/- 0.00 |
| name: Expert normalized total reward |
| - type: human_normalized_total_reward |
| value: 0.00 +/- 0.00 |
| name: Human normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Video Pinball |
| type: atari-videopinball |
| metrics: |
| - type: total_reward |
| value: 12466.69 +/- 8723.07 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 0.03 +/- 0.02 |
| name: Expert normalized total reward |
| - type: human_normalized_total_reward |
| value: 0.71 +/- 0.49 |
| name: Human normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Wizard of Wor |
| type: atari-wizardofwor |
| metrics: |
| - type: total_reward |
| value: 2231.00 +/- 2042.92 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 0.03 +/- 0.04 |
| name: Expert normalized total reward |
| - type: human_normalized_total_reward |
| value: 0.40 +/- 0.49 |
| name: Human normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Yars Revenge |
| type: atari-yarsrevenge |
| metrics: |
| - type: total_reward |
| value: 11190.85 +/- 7342.58 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 0.03 +/- 0.03 |
| name: Expert normalized total reward |
| - type: human_normalized_total_reward |
| value: 0.16 +/- 0.14 |
| name: Human normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Zaxxon |
| type: atari-zaxxon |
| metrics: |
| - type: total_reward |
| value: 5976.00 +/- 2889.54 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 0.08 +/- 0.04 |
| name: Expert normalized total reward |
| - type: human_normalized_total_reward |
| value: 0.65 +/- 0.32 |
| name: Human normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Action Obj Door |
| type: babyai-action-obj-door |
| metrics: |
| - type: total_reward |
| value: 0.92 +/- 0.22 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 0.88 +/- 0.36 |
| name: Expert normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Blocked Unlock Pickup |
| type: babyai-blocked-unlock-pickup |
| metrics: |
| - type: total_reward |
| value: 0.95 +/- 0.01 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 1.00 +/- 0.01 |
| name: Expert normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Boss Level No Unlock |
| type: babyai-boss-level-no-unlock |
| metrics: |
| - type: total_reward |
| value: 0.49 +/- 0.43 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 0.49 +/- 0.49 |
| name: Expert normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Boss Level |
| type: babyai-boss-level |
| metrics: |
| - type: total_reward |
| value: 0.54 +/- 0.43 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 0.54 +/- 0.49 |
| name: Expert normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Find Obj S5 |
| type: babyai-find-obj-s5 |
| metrics: |
| - type: total_reward |
| value: 0.94 +/- 0.04 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 1.00 +/- 0.04 |
| name: Expert normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Go To Door |
| type: babyai-go-to-door |
| metrics: |
| - type: total_reward |
| value: 0.99 +/- 0.02 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 1.00 +/- 0.03 |
| name: Expert normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Go To Imp Unlock |
| type: babyai-go-to-imp-unlock |
| metrics: |
| - type: total_reward |
| value: 0.53 +/- 0.41 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 0.60 +/- 0.55 |
| name: Expert normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Go To Local |
| type: babyai-go-to-local |
| metrics: |
| - type: total_reward |
| value: 0.87 +/- 0.16 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 0.93 +/- 0.22 |
| name: Expert normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Go To Obj Door |
| type: babyai-go-to-obj-door |
| metrics: |
| - type: total_reward |
| value: 0.98 +/- 0.04 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 0.98 +/- 0.08 |
| name: Expert normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Go To Obj |
| type: babyai-go-to-obj |
| metrics: |
| - type: total_reward |
| value: 0.94 +/- 0.03 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 1.01 +/- 0.03 |
| name: Expert normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Go To Red Ball Grey |
| type: babyai-go-to-red-ball-grey |
| metrics: |
| - type: total_reward |
| value: 0.92 +/- 0.05 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 1.00 +/- 0.06 |
| name: Expert normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Go To Red Ball No Dists |
| type: babyai-go-to-red-ball-no-dists |
| metrics: |
| - type: total_reward |
| value: 0.93 +/- 0.03 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 1.00 +/- 0.03 |
| name: Expert normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Go To Red Ball |
| type: babyai-go-to-red-ball |
| metrics: |
| - type: total_reward |
| value: 0.91 +/- 0.09 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 0.98 +/- 0.12 |
| name: Expert normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Go To Red Blue Ball |
| type: babyai-go-to-red-blue-ball |
| metrics: |
| - type: total_reward |
| value: 0.91 +/- 0.08 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 0.99 +/- 0.10 |
| name: Expert normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Go To Seq |
| type: babyai-go-to-seq |
| metrics: |
| - type: total_reward |
| value: 0.73 +/- 0.33 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 0.76 +/- 0.38 |
| name: Expert normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Go To |
| type: babyai-go-to |
| metrics: |
| - type: total_reward |
| value: 0.78 +/- 0.28 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 0.82 +/- 0.35 |
| name: Expert normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Key Corridor |
| type: babyai-key-corridor |
| metrics: |
| - type: total_reward |
| value: 0.87 +/- 0.13 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 0.96 +/- 0.14 |
| name: Expert normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Mini Boss Level |
| type: babyai-mini-boss-level |
| metrics: |
| - type: total_reward |
| value: 0.53 +/- 0.41 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 0.56 +/- 0.50 |
| name: Expert normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Move Two Across S8N9 |
| type: babyai-move-two-across-s8n9 |
| metrics: |
| - type: total_reward |
| value: 0.05 +/- 0.19 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 0.05 +/- 0.20 |
| name: Expert normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: One Room S8 |
| type: babyai-one-room-s8 |
| metrics: |
| - type: total_reward |
| value: 0.92 +/- 0.04 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 1.00 +/- 0.04 |
| name: Expert normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Open Door |
| type: babyai-open-door |
| metrics: |
| - type: total_reward |
| value: 0.99 +/- 0.00 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 1.00 +/- 0.01 |
| name: Expert normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Open Doors Order N4 |
| type: babyai-open-doors-order-n4 |
| metrics: |
| - type: total_reward |
| value: 0.96 +/- 0.14 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 0.96 +/- 0.17 |
| name: Expert normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Open Red Door |
| type: babyai-open-red-door |
| metrics: |
| - type: total_reward |
| value: 0.92 +/- 0.03 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 1.00 +/- 0.03 |
| name: Expert normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Open Two Doors |
| type: babyai-open-two-doors |
| metrics: |
| - type: total_reward |
| value: 0.98 +/- 0.00 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 1.00 +/- 0.00 |
| name: Expert normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Open |
| type: babyai-open |
| metrics: |
| - type: total_reward |
| value: 0.95 +/- 0.08 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 0.99 +/- 0.10 |
| name: Expert normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Pickup Above |
| type: babyai-pickup-above |
| metrics: |
| - type: total_reward |
| value: 0.92 +/- 0.06 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 1.01 +/- 0.07 |
| name: Expert normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Pickup Dist |
| type: babyai-pickup-dist |
| metrics: |
| - type: total_reward |
| value: 0.87 +/- 0.12 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 1.02 +/- 0.16 |
| name: Expert normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Pickup Loc |
| type: babyai-pickup-loc |
| metrics: |
| - type: total_reward |
| value: 0.85 +/- 0.19 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 0.92 +/- 0.23 |
| name: Expert normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Pickup |
| type: babyai-pickup |
| metrics: |
| - type: total_reward |
| value: 0.79 +/- 0.30 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 0.85 +/- 0.36 |
| name: Expert normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Put Next Local |
| type: babyai-put-next-local |
| metrics: |
| - type: total_reward |
| value: 0.67 +/- 0.32 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 0.73 +/- 0.35 |
| name: Expert normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Put Next S7N4 |
| type: babyai-put-next |
| metrics: |
| - type: total_reward |
| value: 0.85 +/- 0.25 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 0.89 +/- 0.26 |
| name: Expert normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Synth Loc |
| type: babyai-synth-loc |
| metrics: |
| - type: total_reward |
| value: 0.77 +/- 0.34 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 0.78 +/- 0.43 |
| name: Expert normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Synth Seq |
| type: babyai-synth-seq |
| metrics: |
| - type: total_reward |
| value: 0.57 +/- 0.43 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 0.58 +/- 0.49 |
| name: Expert normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Synth |
| type: babyai-synth |
| metrics: |
| - type: total_reward |
| value: 0.75 +/- 0.35 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 0.78 +/- 0.43 |
| name: Expert normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Unblock Pickup |
| type: babyai-unblock-pickup |
| metrics: |
| - type: total_reward |
| value: 0.79 +/- 0.29 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 0.86 +/- 0.35 |
| name: Expert normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Unlock Local |
| type: babyai-unlock-local |
| metrics: |
| - type: total_reward |
| value: 0.98 +/- 0.01 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 1.00 +/- 0.01 |
| name: Expert normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Unlock Pickup |
| type: babyai-unlock-pickup |
| metrics: |
| - type: total_reward |
| value: 0.75 +/- 0.03 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 1.00 +/- 0.05 |
| name: Expert normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Unlock To Unlock |
| type: babyai-unlock-to-unlock |
| metrics: |
| - type: total_reward |
| value: 0.85 +/- 0.31 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 0.88 +/- 0.32 |
| name: Expert normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Unlock |
| type: babyai-unlock |
| metrics: |
| - type: total_reward |
| value: 0.43 +/- 0.43 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 0.48 +/- 0.52 |
| name: Expert normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Assembly |
| type: metaworld-assembly |
| metrics: |
| - type: total_reward |
| value: 243.78 +/- 10.44 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 0.99 +/- 0.05 |
| name: Expert normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Basketball |
| type: metaworld-basketball |
| metrics: |
| - type: total_reward |
| value: 1.71 +/- 0.63 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: -0.00 +/- 0.00 |
| name: Expert normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: BinPicking |
| type: metaworld-bin-picking |
| metrics: |
| - type: total_reward |
| value: 314.42 +/- 196.40 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 0.74 +/- 0.46 |
| name: Expert normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Box Close |
| type: metaworld-box-close |
| metrics: |
| - type: total_reward |
| value: 482.86 +/- 146.37 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 0.93 +/- 0.34 |
| name: Expert normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Button Press Topdown Wall |
| type: metaworld-button-press-topdown-wall |
| metrics: |
| - type: total_reward |
| value: 268.30 +/- 82.78 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 0.51 +/- 0.18 |
| name: Expert normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Button Press Topdown |
| type: metaworld-button-press-topdown |
| metrics: |
| - type: total_reward |
| value: 269.14 +/- 82.81 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 0.52 +/- 0.18 |
| name: Expert normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Button Press Wall |
| type: metaworld-button-press-wall |
| metrics: |
| - type: total_reward |
| value: 608.87 +/- 169.50 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 0.90 +/- 0.25 |
| name: Expert normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Button Press |
| type: metaworld-button-press |
| metrics: |
| - type: total_reward |
| value: 624.03 +/- 73.53 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 0.97 +/- 0.12 |
| name: Expert normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Coffee Button |
| type: metaworld-coffee-button |
| metrics: |
| - type: total_reward |
| value: 334.92 +/- 301.67 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 0.43 +/- 0.43 |
| name: Expert normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Coffee Pull |
| type: metaworld-coffee-pull |
| metrics: |
| - type: total_reward |
| value: 38.00 +/- 63.97 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 0.13 +/- 0.25 |
| name: Expert normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Coffee Push |
| type: metaworld-coffee-push |
| metrics: |
| - type: total_reward |
| value: 151.38 +/- 207.69 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 0.30 +/- 0.42 |
| name: Expert normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Dial Turn |
| type: metaworld-dial-turn |
| metrics: |
| - type: total_reward |
| value: 752.25 +/- 138.50 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 0.95 +/- 0.18 |
| name: Expert normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Disassemble |
| type: metaworld-disassemble |
| metrics: |
| - type: total_reward |
| value: 40.87 +/- 9.35 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 0.22 +/- 3.71 |
| name: Expert normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Door Close |
| type: metaworld-door-close |
| metrics: |
| - type: total_reward |
| value: 530.48 +/- 29.02 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 1.00 +/- 0.06 |
| name: Expert normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Door Lock |
| type: metaworld-door-lock |
| metrics: |
| - type: total_reward |
| value: 678.98 +/- 194.57 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 0.81 +/- 0.28 |
| name: Expert normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Door Open |
| type: metaworld-door-open |
| metrics: |
| - type: total_reward |
| value: 574.71 +/- 50.82 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 0.99 +/- 0.10 |
| name: Expert normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Door Unlock |
| type: metaworld-door-unlock |
| metrics: |
| - type: total_reward |
| value: 761.82 +/- 114.70 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 0.94 +/- 0.16 |
| name: Expert normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Drawer Close |
| type: metaworld-drawer-close |
| metrics: |
| - type: total_reward |
| value: 519.05 +/- 154.38 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 0.54 +/- 0.21 |
| name: Expert normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Drawer Open |
| type: metaworld-drawer-open |
| metrics: |
| - type: total_reward |
| value: 486.02 +/- 34.17 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 0.98 +/- 0.09 |
| name: Expert normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Faucet Close |
| type: metaworld-faucet-close |
| metrics: |
| - type: total_reward |
| value: 366.78 +/- 86.77 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 0.23 +/- 0.17 |
| name: Expert normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Faucet Open |
| type: metaworld-faucet-open |
| metrics: |
| - type: total_reward |
| value: 685.01 +/- 65.52 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 0.96 +/- 0.14 |
| name: Expert normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Hammer |
| type: metaworld-hammer |
| metrics: |
| - type: total_reward |
| value: 678.36 +/- 79.36 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 0.98 +/- 0.13 |
| name: Expert normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Hand Insert |
| type: metaworld-hand-insert |
| metrics: |
| - type: total_reward |
| value: 695.27 +/- 134.25 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 0.94 +/- 0.18 |
| name: Expert normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Handle Press Side |
| type: metaworld-handle-press-side |
| metrics: |
| - type: total_reward |
| value: 65.07 +/- 69.65 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 0.01 +/- 0.09 |
| name: Expert normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Handle Press |
| type: metaworld-handle-press |
| metrics: |
| - type: total_reward |
| value: 695.97 +/- 311.48 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 0.79 +/- 0.40 |
| name: Expert normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Handle Pull Side |
| type: metaworld-handle-pull-side |
| metrics: |
| - type: total_reward |
| value: 145.34 +/- 179.01 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 0.37 +/- 0.47 |
| name: Expert normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Handle Pull |
| type: metaworld-handle-pull |
| metrics: |
| - type: total_reward |
| value: 514.56 +/- 205.75 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 0.77 +/- 0.31 |
| name: Expert normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Lever Pull |
| type: metaworld-lever-pull |
| metrics: |
| - type: total_reward |
| value: 250.51 +/- 220.33 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 0.34 +/- 0.40 |
| name: Expert normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Peg Insert Side |
| type: metaworld-peg-insert-side |
| metrics: |
| - type: total_reward |
| value: 305.94 +/- 166.53 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 0.97 +/- 0.53 |
| name: Expert normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Peg Unplug Side |
| type: metaworld-peg-unplug-side |
| metrics: |
| - type: total_reward |
| value: 120.73 +/- 169.26 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 0.26 +/- 0.37 |
| name: Expert normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Pick Out Of Hole |
| type: metaworld-pick-out-of-hole |
| metrics: |
| - type: total_reward |
| value: 2.08 +/- 0.05 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 0.00 +/- 0.00 |
| name: Expert normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Pick Place Wall |
| type: metaworld-pick-place-wall |
| metrics: |
| - type: total_reward |
| value: 62.30 +/- 131.13 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 0.14 +/- 0.29 |
| name: Expert normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Pick Place |
| type: metaworld-pick-place |
| metrics: |
| - type: total_reward |
| value: 311.95 +/- 180.95 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 0.74 +/- 0.43 |
| name: Expert normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Plate Slide Back Side |
| type: metaworld-plate-slide-back-side |
| metrics: |
| - type: total_reward |
| value: 689.54 +/- 157.90 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 0.94 +/- 0.23 |
| name: Expert normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Plate Slide Back |
| type: metaworld-plate-slide-back |
| metrics: |
| - type: total_reward |
| value: 197.00 +/- 1.58 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 0.24 +/- 0.00 |
| name: Expert normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Plate Slide Side |
| type: metaworld-plate-slide-side |
| metrics: |
| - type: total_reward |
| value: 122.56 +/- 24.56 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 0.16 +/- 0.04 |
| name: Expert normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Plate Slide |
| type: metaworld-plate-slide |
| metrics: |
| - type: total_reward |
| value: 456.66 +/- 198.51 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 0.84 +/- 0.44 |
| name: Expert normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Push Back |
| type: metaworld-push-back |
| metrics: |
| - type: total_reward |
| value: 71.38 +/- 100.60 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 0.84 +/- 1.20 |
| name: Expert normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Push Wall |
| type: metaworld-push-wall |
| metrics: |
| - type: total_reward |
| value: 216.66 +/- 256.33 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 0.28 +/- 0.35 |
| name: Expert normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Push |
| type: metaworld-push |
| metrics: |
| - type: total_reward |
| value: 583.25 +/- 296.10 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 0.78 +/- 0.40 |
| name: Expert normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Reach Wall |
| type: metaworld-reach-wall |
| metrics: |
| - type: total_reward |
| value: 681.90 +/- 186.63 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 0.89 +/- 0.31 |
| name: Expert normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Reach |
| type: metaworld-reach |
| metrics: |
| - type: total_reward |
| value: 347.45 +/- 190.66 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 0.37 +/- 0.36 |
| name: Expert normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Shelf Place |
| type: metaworld-shelf-place |
| metrics: |
| - type: total_reward |
| value: 60.57 +/- 97.16 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 0.25 +/- 0.40 |
| name: Expert normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Soccer |
| type: metaworld-soccer |
| metrics: |
| - type: total_reward |
| value: 309.21 +/- 172.64 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 0.82 +/- 0.47 |
| name: Expert normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Stick Pull |
| type: metaworld-stick-pull |
| metrics: |
| - type: total_reward |
| value: 364.98 +/- 234.82 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 0.70 +/- 0.45 |
| name: Expert normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Stick Push |
| type: metaworld-stick-push |
| metrics: |
| - type: total_reward |
| value: 91.05 +/- 204.71 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 0.14 +/- 0.33 |
| name: Expert normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Sweep Into |
| type: metaworld-sweep-into |
| metrics: |
| - type: total_reward |
| value: 714.98 +/- 209.19 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 0.89 +/- 0.27 |
| name: Expert normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Sweep |
| type: metaworld-sweep |
| metrics: |
| - type: total_reward |
| value: 15.82 +/- 16.34 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 0.01 +/- 0.03 |
| name: Expert normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Window Close |
| type: metaworld-window-close |
| metrics: |
| - type: total_reward |
| value: 347.90 +/- 222.50 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 0.54 +/- 0.42 |
| name: Expert normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Window Open |
| type: metaworld-window-open |
| metrics: |
| - type: total_reward |
| value: 574.72 +/- 75.65 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 0.97 +/- 0.14 |
| name: Expert normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Ant |
| type: mujoco-ant |
| metrics: |
| - type: total_reward |
| value: 4993.13 +/- 1656.89 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 0.86 +/- 0.28 |
| name: Expert normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Inverted Double Pendulum |
| type: mujoco-doublependulum |
| metrics: |
| - type: total_reward |
| value: 8744.92 +/- 1471.45 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 0.94 +/- 0.16 |
| name: Expert normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Half Cheetah |
| type: mujoco-halfcheetah |
| metrics: |
| - type: total_reward |
| value: 6601.12 +/- 488.36 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 0.89 +/- 0.06 |
| name: Expert normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Hopper |
| type: mujoco-hopper |
| metrics: |
| - type: total_reward |
| value: 1435.45 +/- 361.77 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 0.77 +/- 0.20 |
| name: Expert normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Humanoid |
| type: mujoco-humanoid |
| metrics: |
| - type: total_reward |
| value: 695.92 +/- 115.07 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 0.09 +/- 0.02 |
| name: Expert normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Inverted Pendulum |
| type: mujoco-pendulum |
| metrics: |
| - type: total_reward |
| value: 117.64 +/- 21.73 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 0.24 +/- 0.05 |
| name: Expert normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Pusher |
| type: mujoco-pusher |
| metrics: |
| - type: total_reward |
| value: -24.93 +/- 6.47 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 1.00 +/- 0.05 |
| name: Expert normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Reacher |
| type: mujoco-reacher |
| metrics: |
| - type: total_reward |
| value: -5.77 +/- 2.27 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 1.00 +/- 0.06 |
| name: Expert normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Humanoid Standup |
| type: mujoco-standup |
| metrics: |
| - type: total_reward |
| value: 113587.22 +/- 21821.69 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 0.33 +/- 0.09 |
| name: Expert normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Swimmer |
| type: mujoco-swimmer |
| metrics: |
| - type: total_reward |
| value: 94.08 +/- 3.94 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 1.02 +/- 0.04 |
| name: Expert normalized total reward |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: Walker 2d |
| type: mujoco-walker |
| metrics: |
| - type: total_reward |
| value: 4381.69 +/- 848.39 |
| name: Total reward |
| - type: expert_normalized_total_reward |
| value: 0.95 +/- 0.18 |
| name: Expert normalized total reward |
| --- |
| |
| # Model Card for Jat |
|
|
| This is a multi-modal and multi-task model. |
|
|
| ## Model Details |
|
|
| ### Model Description |
|
|
| - **Developed by:** The JAT Team |
| - **License:** Apache 2.0 |
|
|
| ### Model Sources |
|
|
| - **Repository:** <https://github.com/huggingface/jat> |
| - **Paper:** <https://huggingface.co/papers/2402.09844> |
| - **Demo:** Coming soon |
|
|
| ## Training |
|
|
| <details> |
| <summary>The model was trained on the following tasks:</summary> |
| |
| - Alien |
| - Amidar |
| - Assault |
| - Asterix |
| - Asteroids |
| - Atlantis |
| - Bank Heist |
| - Battle Zone |
| - Beam Rider |
| - Berzerk |
| - Bowling |
| - Boxing |
| - Breakout |
| - Centipede |
| - Chopper Command |
| - Crazy Climber |
| - Defender |
| - Demon Attack |
| - Double Dunk |
| - Enduro |
| - Fishing Derby |
| - Freeway |
| - Frostbite |
| - Gopher |
| - Gravitar |
| - H.E.R.O. |
| - Ice Hockey |
| - James Bond |
| - Kangaroo |
| - Krull |
| - Kung-Fu Master |
| - Montezuma's Revenge |
| - Ms. Pacman |
| - Name This Game |
| - Phoenix |
| - PitFall |
| - Pong |
| - Private Eye |
| - Q*Bert |
| - River Raid |
| - Road Runner |
| - Robotank |
| - Seaquest |
| - Skiing |
| - Solaris |
| - Space Invaders |
| - Star Gunner |
| - Surround |
| - Tennis |
| - Time Pilot |
| - Tutankham |
| - Up and Down |
| - Venture |
| - Video Pinball |
| - Wizard of Wor |
| - Yars Revenge |
| - Zaxxon |
| - Action Obj Door |
| - Blocked Unlock Pickup |
| - Boss Level No Unlock |
| - Boss Level |
| - Find Obj S5 |
| - Go To Door |
| - Go To Imp Unlock |
| - Go To Local |
| - Go To Obj Door |
| - Go To Obj |
| - Go To Red Ball Grey |
| - Go To Red Ball No Dists |
| - Go To Red Ball |
| - Go To Red Blue Ball |
| - Go To Seq |
| - Go To |
| - Key Corridor |
| - Mini Boss Level |
| - Move Two Across S8N9 |
| - One Room S8 |
| - Open Door |
| - Open Doors Order N4 |
| - Open Red Door |
| - Open Two Doors |
| - Open |
| - Pickup Above |
| - Pickup Dist |
| - Pickup Loc |
| - Pickup |
| - Put Next Local |
| - Put Next S7N4 |
| - Synth Loc |
| - Synth Seq |
| - Synth |
| - Unblock Pickup |
| - Unlock Local |
| - Unlock Pickup |
| - Unlock To Unlock |
| - Unlock |
| - Assembly |
| - Basketball |
| - BinPicking |
| - Box Close |
| - Button Press Topdown Wall |
| - Button Press Topdown |
| - Button Press Wall |
| - Button Press |
| - Coffee Button |
| - Coffee Pull |
| - Coffee Push |
| - Dial Turn |
| - Disassemble |
| - Door Close |
| - Door Lock |
| - Door Open |
| - Door Unlock |
| - Drawer Close |
| - Drawer Open |
| - Faucet Close |
| - Faucet Open |
| - Hammer |
| - Hand Insert |
| - Handle Press Side |
| - Handle Press |
| - Handle Pull Side |
| - Handle Pull |
| - Lever Pull |
| - Peg Insert Side |
| - Peg Unplug Side |
| - Pick Out Of Hole |
| - Pick Place Wall |
| - Pick Place |
| - Plate Slide Back Side |
| - Plate Slide Back |
| - Plate Slide Side |
| - Plate Slide |
| - Push Back |
| - Push Wall |
| - Push |
| - Reach Wall |
| - Reach |
| - Shelf Place |
| - Soccer |
| - Stick Pull |
| - Stick Push |
| - Sweep Into |
| - Sweep |
| - Window Close |
| - Window Open |
| - Ant |
| - Inverted Double Pendulum |
| - Half Cheetah |
| - Hopper |
| - Humanoid |
| - Inverted Pendulum |
| - Pusher |
| - Reacher |
| - Humanoid Standup |
| - Swimmer |
| - Walker 2d |
| |
| </details> |
| |
| ## How to Get Started with the Model |
| |
| Use the code below to get started with the model. |
| |
| ```python |
| from transformers import AutoModelForCausalLM |
| |
| model = AutoModelForCausalLM.from_pretrained("jat-project/jat") |
| ``` |
| |
| ## Citation |
| |
| |
| ```bibtex |
| @article{gallouedec2024jack, |
| title = {{Jack of All Trades, Master of Some, a Multi-Purpose Transformer Agent}}, |
| author = {Gallouédec, Quentin and Beeching, Edward and Romac, Clément and Dellandréa, Emmanuel}, |
| journal = {arXiv preprint arXiv:2402.09844}, |
| year = {2024}, |
| url = {https://arxiv.org/abs/2402.09844} |
| } |
| ``` |
| |
