metadata
tags:
- reinforcement-learning
- atari-alien
- atari-amidar
- atari-assault
- atari-asterix
- atari-asteroids
- atari-atlantis
- atari-bankheist
- atari-battlezone
- atari-beamrider
- atari-berzerk
- atari-bowling
- atari-boxing
- atari-breakout
- atari-centipede
- atari-choppercommand
- atari-crazyclimber
- atari-defender
- atari-demonattack
- atari-doubledunk
- atari-enduro
- atari-fishingderby
- atari-freeway
- atari-frostbite
- atari-gopher
- atari-gravitar
- atari-hero
- atari-icehockey
- atari-jamesbond
- atari-kangaroo
- atari-krull
- atari-kungfumaster
- atari-montezumarevenge
- atari-mspacman
- atari-namethisgame
- atari-phoenix
- atari-pitfall
- atari-pong
- atari-privateeye
- atari-qbert
- atari-riverraid
- atari-roadrunner
- atari-robotank
- atari-seaquest
- atari-skiing
- atari-solaris
- atari-spaceinvaders
- atari-stargunner
- atari-surround
- atari-tennis
- atari-timepilot
- atari-tutankham
- atari-upndown
- atari-venture
- atari-videopinball
- atari-wizardofwor
- atari-yarsrevenge
- atari-zaxxon
- babyai-action-obj-door
- babyai-blocked-unlock-pickup
- babyai-boss-level-no-unlock
- babyai-boss-level
- babyai-find-obj-s5
- babyai-go-to-door
- babyai-go-to-imp-unlock
- babyai-go-to-local
- babyai-go-to-obj-door
- babyai-go-to-obj
- babyai-go-to-red-ball-grey
- babyai-go-to-red-ball-no-dists
- babyai-go-to-red-ball
- babyai-go-to-red-blue-ball
- babyai-go-to-seq
- babyai-go-to
- babyai-key-corridor
- babyai-mini-boss-level
- babyai-move-two-across-s8n9
- babyai-one-room-s8
- babyai-open-door
- babyai-open-doors-order-n4
- babyai-open-red-door
- babyai-open-two-doors
- babyai-open
- babyai-pickup-above
- babyai-pickup-dist
- babyai-pickup-loc
- babyai-pickup
- babyai-put-next-local
- babyai-put-next
- babyai-synth-loc
- babyai-synth-seq
- babyai-synth
- babyai-unblock-pickup
- babyai-unlock-local
- babyai-unlock-pickup
- babyai-unlock-to-unlock
- babyai-unlock
- metaworld-assembly
- metaworld-basketball
- metaworld-bin-picking
- metaworld-box-close
- metaworld-button-press-topdown-wall
- metaworld-button-press-topdown
- metaworld-button-press-wall
- metaworld-button-press
- metaworld-coffee-button
- metaworld-coffee-pull
- metaworld-coffee-push
- metaworld-dial-turn
- metaworld-disassemble
- metaworld-door-close
- metaworld-door-lock
- metaworld-door-open
- metaworld-door-unlock
- metaworld-drawer-close
- metaworld-drawer-open
- metaworld-faucet-close
- metaworld-faucet-open
- metaworld-hammer
- metaworld-hand-insert
- metaworld-handle-press-side
- metaworld-handle-press
- metaworld-handle-pull-side
- metaworld-handle-pull
- metaworld-lever-pull
- metaworld-peg-insert-side
- metaworld-peg-unplug-side
- metaworld-pick-out-of-hole
- metaworld-pick-place-wall
- metaworld-pick-place
- metaworld-plate-slide-back-side
- metaworld-plate-slide-back
- metaworld-plate-slide-side
- metaworld-plate-slide
- metaworld-push-back
- metaworld-push-wall
- metaworld-push
- metaworld-reach-wall
- metaworld-reach
- metaworld-shelf-place
- metaworld-soccer
- metaworld-stick-pull
- metaworld-stick-push
- metaworld-sweep-into
- metaworld-sweep
- metaworld-window-close
- metaworld-window-open
- mujoco-ant
- mujoco-doublependulum
- mujoco-halfcheetah
- mujoco-hopper
- mujoco-humanoid
- mujoco-pendulum
- mujoco-pusher
- mujoco-reacher
- mujoco-standup
- mujoco-swimmer
- mujoco-walker
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: 0.06 [0.06, 0.06]
name: IQM expert normalized total reward
- type: iqm_human_normalized_total_reward
value: 0.17 [0.16, 0.17]
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: 0.68 [0.67, 0.69]
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: 0.81 [0.80, 0.82]
name: IQM expert normalized total reward
- task:
type: reinforcement-learning
name: Reinforcement Learning
dataset:
name: Alien
type: atari-alien
metrics:
- type: total_reward
value: 1085.90 +/- 396.36
name: Total reward
- type: expert_normalized_total_reward
value: 0.05 +/- 0.02
name: Expert normalized total reward
- type: human_normalized_total_reward
value: 0.12 +/- 0.06
name: Human normalized total reward
- task:
type: reinforcement-learning
name: Reinforcement Learning
dataset:
name: Amidar
type: atari-amidar
metrics:
- type: total_reward
value: 41.26 +/- 28.57
name: Total reward
- type: expert_normalized_total_reward
value: 0.02 +/- 0.01
name: Expert normalized total reward
- type: human_normalized_total_reward
value: 0.02 +/- 0.02
name: Human normalized total reward
- task:
type: reinforcement-learning
name: Reinforcement Learning
dataset:
name: Assault
type: atari-assault
metrics:
- type: total_reward
value: 772.89 +/- 59.34
name: Total reward
- type: expert_normalized_total_reward
value: 0.04 +/- 0.00
name: Expert normalized total reward
- type: human_normalized_total_reward
value: 1.06 +/- 0.11
name: Human normalized total reward
- task:
type: reinforcement-learning
name: Reinforcement Learning
dataset:
name: Asterix
type: atari-asterix
metrics:
- type: total_reward
value: 778.50 +/- 428.97
name: Total reward
- type: expert_normalized_total_reward
value: 0.16 +/- 0.12
name: Expert normalized total reward
- type: human_normalized_total_reward
value: 0.07 +/- 0.05
name: Human normalized total reward
- task:
type: reinforcement-learning
name: Reinforcement Learning
dataset:
name: Asteroids
type: atari-asteroids
metrics:
- type: total_reward
value: 1423.60 +/- 538.79
name: Total reward
- type: expert_normalized_total_reward
value: 0.00 +/- 0.00
name: Expert normalized total reward
- type: human_normalized_total_reward
value: 0.02 +/- 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: 23541.00 +/- 10376.72
name: Total reward
- type: expert_normalized_total_reward
value: 0.03 +/- 0.03
name: Expert normalized total reward
- type: human_normalized_total_reward
value: 0.66 +/- 0.64
name: Human normalized total reward
- task:
type: reinforcement-learning
name: Reinforcement Learning
dataset:
name: Bank Heist
type: atari-bankheist
metrics:
- type: total_reward
value: 685.50 +/- 157.92
name: Total reward
- type: expert_normalized_total_reward
value: 0.51 +/- 0.12
name: Expert normalized total reward
- type: human_normalized_total_reward
value: 0.91 +/- 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: 12950.00 +/- 4306.68
name: Total reward
- type: expert_normalized_total_reward
value: 0.04 +/- 0.01
name: Expert normalized total reward
- type: human_normalized_total_reward
value: 0.34 +/- 0.12
name: Human normalized total reward
- task:
type: reinforcement-learning
name: Reinforcement Learning
dataset:
name: Beam Rider
type: atari-beamrider
metrics:
- type: total_reward
value: 762.04 +/- 243.25
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 +/- 0.01
name: Human normalized total reward
- task:
type: reinforcement-learning
name: Reinforcement Learning
dataset:
name: Berzerk
type: atari-berzerk
metrics:
- type: total_reward
value: 523.90 +/- 161.95
name: Total reward
- type: expert_normalized_total_reward
value: 0.01 +/- 0.00
name: Expert normalized total reward
- type: human_normalized_total_reward
value: 0.16 +/- 0.06
name: Human normalized total reward
- task:
type: reinforcement-learning
name: Reinforcement Learning
dataset:
name: Bowling
type: atari-bowling
metrics:
- type: total_reward
value: 29.99 +/- 11.49
name: Total reward
- type: expert_normalized_total_reward
value: 1.00 +/- 0.00
name: Expert normalized total reward
- type: human_normalized_total_reward
value: 0.05 +/- 0.08
name: Human normalized total reward
- task:
type: reinforcement-learning
name: Reinforcement Learning
dataset:
name: Boxing
type: atari-boxing
metrics:
- type: total_reward
value: 87.00 +/- 22.57
name: Total reward
- type: expert_normalized_total_reward
value: 0.89 +/- 0.23
name: Expert normalized total reward
- type: human_normalized_total_reward
value: 7.24 +/- 1.88
name: Human normalized total reward
- task:
type: reinforcement-learning
name: Reinforcement Learning
dataset:
name: Breakout
type: atari-breakout
metrics:
- type: total_reward
value: 9.16 +/- 5.76
name: Total reward
- type: expert_normalized_total_reward
value: 0.01 +/- 0.01
name: Expert normalized total reward
- type: human_normalized_total_reward
value: 0.26 +/- 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: 4461.72 +/- 2188.80
name: Total reward
- type: expert_normalized_total_reward
value: 0.25 +/- 0.23
name: Expert normalized total reward
- type: human_normalized_total_reward
value: 0.24 +/- 0.22
name: Human normalized total reward
- task:
type: reinforcement-learning
name: Reinforcement Learning
dataset:
name: Chopper Command
type: atari-choppercommand
metrics:
- type: total_reward
value: 1497.00 +/- 723.11
name: Total reward
- type: expert_normalized_total_reward
value: 0.01 +/- 0.01
name: Expert normalized total reward
- type: human_normalized_total_reward
value: 0.10 +/- 0.11
name: Human normalized total reward
- task:
type: reinforcement-learning
name: Reinforcement Learning
dataset:
name: Crazy Climber
type: atari-crazyclimber
metrics:
- type: total_reward
value: 52850.00 +/- 31617.86
name: Total reward
- type: expert_normalized_total_reward
value: 0.25 +/- 0.19
name: Expert normalized total reward
- type: human_normalized_total_reward
value: 1.68 +/- 1.26
name: Human normalized total reward
- task:
type: reinforcement-learning
name: Reinforcement Learning
dataset:
name: Defender
type: atari-defender
metrics:
- type: total_reward
value: 10627.50 +/- 4473.21
name: Total reward
- type: expert_normalized_total_reward
value: 0.02 +/- 0.01
name: Expert normalized total reward
- type: human_normalized_total_reward
value: 0.49 +/- 0.28
name: Human normalized total reward
- task:
type: reinforcement-learning
name: Reinforcement Learning
dataset:
name: Demon Attack
type: atari-demonattack
metrics:
- type: total_reward
value: 315.10 +/- 279.01
name: Total reward
- type: expert_normalized_total_reward
value: 0.00 +/- 0.00
name: Expert normalized total reward
- type: human_normalized_total_reward
value: 0.09 +/- 0.15
name: Human normalized total reward
- task:
type: reinforcement-learning
name: Reinforcement Learning
dataset:
name: Double Dunk
type: atari-doubledunk
metrics:
- type: total_reward
value: 0.08 +/- 11.61
name: Total reward
- type: expert_normalized_total_reward
value: 0.47 +/- 0.29
name: Expert normalized total reward
- type: human_normalized_total_reward
value: 0.53 +/- 0.33
name: Human normalized total reward
- task:
type: reinforcement-learning
name: Reinforcement Learning
dataset:
name: Enduro
type: atari-enduro
metrics:
- type: total_reward
value: 111.49 +/- 27.36
name: Total reward
- type: expert_normalized_total_reward
value: 0.05 +/- 0.01
name: Expert normalized total reward
- type: human_normalized_total_reward
value: 0.13 +/- 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: '-55.21 +/- 19.35'
name: Total reward
- type: expert_normalized_total_reward
value: 0.37 +/- 0.20
name: Expert normalized total reward
- type: human_normalized_total_reward
value: 0.28 +/- 0.15
name: Human normalized total reward
- task:
type: reinforcement-learning
name: Reinforcement Learning
dataset:
name: Freeway
type: atari-freeway
metrics:
- type: total_reward
value: 24.12 +/- 1.64
name: Total reward
- type: expert_normalized_total_reward
value: 0.71 +/- 0.05
name: Expert normalized total reward
- type: human_normalized_total_reward
value: 0.81 +/- 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: 617.30 +/- 686.11
name: Total reward
- type: expert_normalized_total_reward
value: 0.04 +/- 0.05
name: Expert normalized total reward
- type: human_normalized_total_reward
value: 0.13 +/- 0.16
name: Human normalized total reward
- task:
type: reinforcement-learning
name: Reinforcement Learning
dataset:
name: Gopher
type: atari-gopher
metrics:
- type: total_reward
value: 2947.20 +/- 1448.32
name: Total reward
- type: expert_normalized_total_reward
value: 0.03 +/- 0.02
name: Expert normalized total reward
- type: human_normalized_total_reward
value: 1.25 +/- 0.67
name: Human normalized total reward
- task:
type: reinforcement-learning
name: Reinforcement Learning
dataset:
name: Gravitar
type: atari-gravitar
metrics:
- type: total_reward
value: 1030.50 +/- 719.20
name: Total reward
- type: expert_normalized_total_reward
value: 0.22 +/- 0.19
name: Expert normalized total reward
- type: human_normalized_total_reward
value: 0.27 +/- 0.23
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: 6997.95 +/- 2562.51
name: Total reward
- type: expert_normalized_total_reward
value: 0.14 +/- 0.06
name: Expert normalized total reward
- type: human_normalized_total_reward
value: 0.20 +/- 0.09
name: Human normalized total reward
- task:
type: reinforcement-learning
name: Reinforcement Learning
dataset:
name: Ice Hockey
type: atari-icehockey
metrics:
- type: total_reward
value: '-3.77 +/- 3.10'
name: Total reward
- type: expert_normalized_total_reward
value: 0.20 +/- 0.09
name: Expert normalized total reward
- type: human_normalized_total_reward
value: 0.61 +/- 0.26
name: Human normalized total reward
- task:
type: reinforcement-learning
name: Reinforcement Learning
dataset:
name: James Bond
type: atari-jamesbond
metrics:
- type: total_reward
value: 187.50 +/- 72.24
name: Total reward
- type: expert_normalized_total_reward
value: 0.01 +/- 0.00
name: Expert normalized total reward
- type: human_normalized_total_reward
value: 0.58 +/- 0.26
name: Human normalized total reward
- task:
type: reinforcement-learning
name: Reinforcement Learning
dataset:
name: Kangaroo
type: atari-kangaroo
metrics:
- type: total_reward
value: 124.00 +/- 156.92
name: Total reward
- type: expert_normalized_total_reward
value: 0.14 +/- 0.30
name: Expert normalized total reward
- type: human_normalized_total_reward
value: 0.02 +/- 0.05
name: Human normalized total reward
- task:
type: reinforcement-learning
name: Reinforcement Learning
dataset:
name: Krull
type: atari-krull
metrics:
- type: total_reward
value: 8933.00 +/- 1358.65
name: Total reward
- type: expert_normalized_total_reward
value: 0.75 +/- 0.14
name: Expert normalized total reward
- type: human_normalized_total_reward
value: 6.87 +/- 1.27
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: 100.00 +/- 142.13
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: 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: 1516.30 +/- 376.72
name: Total reward
- type: expert_normalized_total_reward
value: 0.18 +/- 0.06
name: Expert normalized total reward
- type: human_normalized_total_reward
value: 0.18 +/- 0.06
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: 3798.60 +/- 1361.64
name: Total reward
- type: expert_normalized_total_reward
value: 0.07 +/- 0.07
name: Expert normalized total reward
- type: human_normalized_total_reward
value: 0.26 +/- 0.24
name: Human normalized total reward
- task:
type: reinforcement-learning
name: Reinforcement Learning
dataset:
name: Phoenix
type: atari-phoenix
metrics:
- type: total_reward
value: 1267.50 +/- 1013.72
name: Total reward
- type: expert_normalized_total_reward
value: 0.00 +/- 0.00
name: Expert normalized total reward
- type: human_normalized_total_reward
value: 0.08 +/- 0.16
name: Human normalized total reward
- task:
type: reinforcement-learning
name: Reinforcement Learning
dataset:
name: PitFall
type: atari-pitfall
metrics:
- type: total_reward
value: '-287.36 +/- 492.82'
name: Total reward
- type: expert_normalized_total_reward
value: '-0.25 +/- 2.16'
name: Expert normalized total reward
- type: human_normalized_total_reward
value: '-0.01 +/- 0.07'
name: Human normalized total reward
- task:
type: reinforcement-learning
name: Reinforcement Learning
dataset:
name: Pong
type: atari-pong
metrics:
- type: total_reward
value: '-11.03 +/- 11.29'
name: Total reward
- type: expert_normalized_total_reward
value: 0.23 +/- 0.27
name: Expert normalized total reward
- type: human_normalized_total_reward
value: 0.27 +/- 0.32
name: Human normalized total reward
- task:
type: reinforcement-learning
name: Reinforcement Learning
dataset:
name: Private Eye
type: atari-privateeye
metrics:
- type: total_reward
value: 96.00 +/- 19.60
name: Total reward
- type: expert_normalized_total_reward
value: 0.95 +/- 0.26
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: 1701.75 +/- 1912.56
name: Total reward
- type: expert_normalized_total_reward
value: 0.04 +/- 0.04
name: Expert normalized total reward
- type: human_normalized_total_reward
value: 0.12 +/- 0.14
name: Human normalized total reward
- task:
type: reinforcement-learning
name: Reinforcement Learning
dataset:
name: River Raid
type: atari-riverraid
metrics:
- type: total_reward
value: 2793.10 +/- 693.84
name: Total reward
- type: expert_normalized_total_reward
value: 0.11 +/- 0.05
name: Expert normalized total reward
- type: human_normalized_total_reward
value: 0.09 +/- 0.04
name: Human normalized total reward
- task:
type: reinforcement-learning
name: Reinforcement Learning
dataset:
name: Road Runner
type: atari-roadrunner
metrics:
- type: total_reward
value: 7699.00 +/- 3446.61
name: Total reward
- type: expert_normalized_total_reward
value: 0.10 +/- 0.04
name: Expert normalized total reward
- type: human_normalized_total_reward
value: 0.98 +/- 0.44
name: Human normalized total reward
- task:
type: reinforcement-learning
name: Reinforcement Learning
dataset:
name: Robotank
type: atari-robotank
metrics:
- type: total_reward
value: 16.36 +/- 5.24
name: Total reward
- type: expert_normalized_total_reward
value: 0.18 +/- 0.07
name: Expert normalized total reward
- type: human_normalized_total_reward
value: 1.46 +/- 0.54
name: Human normalized total reward
- task:
type: reinforcement-learning
name: Reinforcement Learning
dataset:
name: Seaquest
type: atari-seaquest
metrics:
- type: total_reward
value: 515.20 +/- 141.51
name: Total reward
- type: expert_normalized_total_reward
value: 0.18 +/- 0.06
name: Expert normalized total reward
- type: human_normalized_total_reward
value: 0.01 +/- 0.00
name: Human normalized total reward
- task:
type: reinforcement-learning
name: Reinforcement Learning
dataset:
name: Skiing
type: atari-skiing
metrics:
- type: total_reward
value: '-29396.08 +/- 3289.80'
name: Total reward
- type: expert_normalized_total_reward
value: '-1.93 +/- 0.52'
name: Expert normalized total reward
- type: human_normalized_total_reward
value: '-0.96 +/- 0.26'
name: Human normalized total reward
- task:
type: reinforcement-learning
name: Reinforcement Learning
dataset:
name: Solaris
type: atari-solaris
metrics:
- type: total_reward
value: 988.20 +/- 487.42
name: Total reward
- type: expert_normalized_total_reward
value: '-2.11 +/- 4.15'
name: Expert normalized total reward
- type: human_normalized_total_reward
value: '-0.02 +/- 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: 339.50 +/- 164.05
name: Total reward
- type: expert_normalized_total_reward
value: 0.01 +/- 0.01
name: Expert normalized total reward
- type: human_normalized_total_reward
value: 0.13 +/- 0.11
name: Human normalized total reward
- task:
type: reinforcement-learning
name: Reinforcement Learning
dataset:
name: Star Gunner
type: atari-stargunner
metrics:
- type: total_reward
value: 978.00 +/- 638.37
name: Total reward
- type: expert_normalized_total_reward
value: 0.00 +/- 0.00
name: Expert normalized total reward
- type: human_normalized_total_reward
value: 0.03 +/- 0.07
name: Human normalized total reward
- task:
type: reinforcement-learning
name: Reinforcement Learning
dataset:
name: Surround
type: atari-surround
metrics:
- type: total_reward
value: '-8.22 +/- 1.19'
name: Total reward
- type: expert_normalized_total_reward
value: 0.09 +/- 0.06
name: Expert normalized total reward
- type: human_normalized_total_reward
value: 0.11 +/- 0.07
name: Human normalized total reward
- task:
type: reinforcement-learning
name: Reinforcement Learning
dataset:
name: Tennis
type: atari-tennis
metrics:
- type: total_reward
value: '-22.38 +/- 2.22'
name: Total reward
- type: expert_normalized_total_reward
value: 0.04 +/- 0.06
name: Expert normalized total reward
- type: human_normalized_total_reward
value: 0.04 +/- 0.07
name: Human normalized total reward
- task:
type: reinforcement-learning
name: Reinforcement Learning
dataset:
name: Time Pilot
type: atari-timepilot
metrics:
- type: total_reward
value: 9534.00 +/- 2577.76
name: Total reward
- type: expert_normalized_total_reward
value: 0.09 +/- 0.04
name: Expert normalized total reward
- type: human_normalized_total_reward
value: 3.59 +/- 1.55
name: Human normalized total reward
- task:
type: reinforcement-learning
name: Reinforcement Learning
dataset:
name: Tutankham
type: atari-tutankham
metrics:
- type: total_reward
value: 40.20 +/- 14.51
name: Total reward
- type: expert_normalized_total_reward
value: 0.10 +/- 0.05
name: Expert normalized total reward
- type: human_normalized_total_reward
value: 0.18 +/- 0.09
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: 6072.00 +/- 2283.30
name: Total reward
- type: expert_normalized_total_reward
value: 0.01 +/- 0.01
name: Expert normalized total reward
- type: human_normalized_total_reward
value: 0.50 +/- 0.20
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: 7943.01 +/- 8351.21
name: Total reward
- type: expert_normalized_total_reward
value: 0.02 +/- 0.02
name: Expert normalized total reward
- type: human_normalized_total_reward
value: 0.45 +/- 0.47
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: 1306.00 +/- 1139.81
name: Total reward
- type: expert_normalized_total_reward
value: 0.02 +/- 0.02
name: Expert normalized total reward
- type: human_normalized_total_reward
value: 0.18 +/- 0.27
name: Human normalized total reward
- task:
type: reinforcement-learning
name: Reinforcement Learning
dataset:
name: Yars Revenge
type: atari-yarsrevenge
metrics:
- type: total_reward
value: 8597.41 +/- 4291.81
name: Total reward
- type: expert_normalized_total_reward
value: 0.02 +/- 0.02
name: Expert normalized total reward
- type: human_normalized_total_reward
value: 0.11 +/- 0.08
name: Human normalized total reward
- task:
type: reinforcement-learning
name: Reinforcement Learning
dataset:
name: Zaxxon
type: atari-zaxxon
metrics:
- type: total_reward
value: 896.00 +/- 1172.68
name: Total reward
- type: expert_normalized_total_reward
value: 0.01 +/- 0.02
name: Expert normalized total reward
- type: human_normalized_total_reward
value: 0.09 +/- 0.13
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: 0.95 +/- 0.13
name: Total reward
- type: expert_normalized_total_reward
value: 0.94 +/- 0.22
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: 0.44 +/- 0.45
name: Total reward
- type: expert_normalized_total_reward
value: 0.43 +/- 0.51
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: 0.48 +/- 0.45
name: Total reward
- type: expert_normalized_total_reward
value: 0.48 +/- 0.51
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: 0.95 +/- 0.03
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 +/- 0.01
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: Go To Imp Unlock
type: babyai-go-to-imp-unlock
metrics:
- type: total_reward
value: 0.50 +/- 0.44
name: Total reward
- type: expert_normalized_total_reward
value: 0.56 +/- 0.59
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: 0.88 +/- 0.14
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: 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: 0.97 +/- 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: 0.93 +/- 0.04
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: Go To Red Ball Grey
type: babyai-go-to-red-ball-grey
metrics:
- type: total_reward
value: 0.91 +/- 0.06
name: Total reward
- type: expert_normalized_total_reward
value: 0.99 +/- 0.08
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 +/- 0.04
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 +/- 0.08
name: Total reward
- type: expert_normalized_total_reward
value: 0.98 +/- 0.11
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: 0.88 +/- 0.11
name: Total reward
- type: expert_normalized_total_reward
value: 0.96 +/- 0.13
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 +/- 0.34
name: Total reward
- type: expert_normalized_total_reward
value: 0.75 +/- 0.40
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: 0.80 +/- 0.27
name: Total reward
- type: expert_normalized_total_reward
value: 0.85 +/- 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: 0.88 +/- 0.10
name: Total reward
- type: expert_normalized_total_reward
value: 0.97 +/- 0.11
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: 0.69 +/- 0.35
name: Total reward
- type: expert_normalized_total_reward
value: 0.76 +/- 0.43
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: 0.03 +/- 0.15
name: Total reward
- type: expert_normalized_total_reward
value: 0.03 +/- 0.16
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 +/- 0.03
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 +/- 0.11
name: Total reward
- type: expert_normalized_total_reward
value: 0.97 +/- 0.13
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 +/- 0.02
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: 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: 0.93 +/- 0.11
name: Total reward
- type: expert_normalized_total_reward
value: 0.97 +/- 0.13
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: 0.88 +/- 0.13
name: Total reward
- type: expert_normalized_total_reward
value: 1.03 +/- 0.18
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: 0.84 +/- 0.20
name: Total reward
- type: expert_normalized_total_reward
value: 0.91 +/- 0.24
name: Expert normalized total reward
- task:
type: reinforcement-learning
name: Reinforcement Learning
dataset:
name: Pickup
type: babyai-pickup
metrics:
- type: total_reward
value: 0.72 +/- 0.34
name: Total reward
- type: expert_normalized_total_reward
value: 0.77 +/- 0.40
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: 0.60 +/- 0.36
name: Total reward
- type: expert_normalized_total_reward
value: 0.65 +/- 0.39
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: 0.82 +/- 0.26
name: Total reward
- type: expert_normalized_total_reward
value: 0.86 +/- 0.27
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: 0.82 +/- 0.31
name: Total reward
- type: expert_normalized_total_reward
value: 0.85 +/- 0.38
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 +/- 0.44
name: Total reward
- type: expert_normalized_total_reward
value: 0.57 +/- 0.50
name: Expert normalized total reward
- task:
type: reinforcement-learning
name: Reinforcement Learning
dataset:
name: Synth
type: babyai-synth
metrics:
- type: total_reward
value: 0.68 +/- 0.39
name: Total reward
- type: expert_normalized_total_reward
value: 0.69 +/- 0.47
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: 0.76 +/- 0.33
name: Total reward
- type: expert_normalized_total_reward
value: 0.82 +/- 0.39
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: 0.76 +/- 0.03
name: Total reward
- type: expert_normalized_total_reward
value: 1.01 +/- 0.04
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: 0.86 +/- 0.29
name: Total reward
- type: expert_normalized_total_reward
value: 0.89 +/- 0.30
name: Expert normalized total reward
- task:
type: reinforcement-learning
name: Reinforcement Learning
dataset:
name: Unlock
type: babyai-unlock
metrics:
- type: total_reward
value: 0.55 +/- 0.42
name: Total reward
- type: expert_normalized_total_reward
value: 0.63 +/- 0.50
name: Expert normalized total reward
- task:
type: reinforcement-learning
name: Reinforcement Learning
dataset:
name: Assembly
type: metaworld-assembly
metrics:
- type: total_reward
value: 238.32 +/- 32.98
name: Total reward
- type: expert_normalized_total_reward
value: 0.96 +/- 0.16
name: Expert normalized total reward
- task:
type: reinforcement-learning
name: Reinforcement Learning
dataset:
name: Basketball
type: metaworld-basketball
metrics:
- type: total_reward
value: 1.59 +/- 0.43
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: 374.18 +/- 168.23
name: Total reward
- type: expert_normalized_total_reward
value: 0.88 +/- 0.40
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: 510.10 +/- 117.47
name: Total reward
- type: expert_normalized_total_reward
value: 0.99 +/- 0.27
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: 260.07 +/- 67.75
name: Total reward
- type: expert_normalized_total_reward
value: 0.49 +/- 0.14
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: 265.16 +/- 77.93
name: Total reward
- type: expert_normalized_total_reward
value: 0.51 +/- 0.17
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: 621.75 +/- 137.13
name: Total reward
- type: expert_normalized_total_reward
value: 0.92 +/- 0.21
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: 556.75 +/- 198.85
name: Total reward
- type: expert_normalized_total_reward
value: 0.86 +/- 0.33
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: 250.50 +/- 266.92
name: Total reward
- type: expert_normalized_total_reward
value: 0.31 +/- 0.38
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: 55.13 +/- 96.96
name: Total reward
- type: expert_normalized_total_reward
value: 0.20 +/- 0.38
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: 269.17 +/- 237.82
name: Total reward
- type: expert_normalized_total_reward
value: 0.54 +/- 0.48
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: 738.22 +/- 168.43
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: Disassemble
type: metaworld-disassemble
metrics:
- type: total_reward
value: 39.14 +/- 11.85
name: Total reward
- type: expert_normalized_total_reward
value: '-0.47 +/- 4.70'
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: 528.17 +/- 29.90
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: 676.51 +/- 192.68
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: 572.76 +/- 57.53
name: Total reward
- type: expert_normalized_total_reward
value: 0.98 +/- 0.11
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: 654.94 +/- 260.64
name: Total reward
- type: expert_normalized_total_reward
value: 0.79 +/- 0.37
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: 663.02 +/- 214.51
name: Total reward
- type: expert_normalized_total_reward
value: 0.73 +/- 0.29
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: 489.07 +/- 21.28
name: Total reward
- type: expert_normalized_total_reward
value: 0.99 +/- 0.06
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: 361.32 +/- 72.28
name: Total reward
- type: expert_normalized_total_reward
value: 0.22 +/- 0.14
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: 637.86 +/- 134.50
name: Total reward
- type: expert_normalized_total_reward
value: 0.85 +/- 0.29
name: Expert normalized total reward
- task:
type: reinforcement-learning
name: Reinforcement Learning
dataset:
name: Hammer
type: metaworld-hammer
metrics:
- type: total_reward
value: 691.72 +/- 25.25
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: Hand Insert
type: metaworld-hand-insert
metrics:
- type: total_reward
value: 719.57 +/- 99.26
name: Total reward
- type: expert_normalized_total_reward
value: 0.97 +/- 0.13
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: 84.25 +/- 113.34
name: Total reward
- type: expert_normalized_total_reward
value: 0.03 +/- 0.14
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: 731.94 +/- 261.90
name: Total reward
- type: expert_normalized_total_reward
value: 0.84 +/- 0.34
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: 233.11 +/- 199.49
name: Total reward
- type: expert_normalized_total_reward
value: 0.60 +/- 0.52
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: 501.29 +/- 209.45
name: Total reward
- type: expert_normalized_total_reward
value: 0.74 +/- 0.32
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: 250.18 +/- 228.59
name: Total reward
- type: expert_normalized_total_reward
value: 0.34 +/- 0.41
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: 288.02 +/- 157.87
name: Total reward
- type: expert_normalized_total_reward
value: 0.91 +/- 0.50
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: 68.48 +/- 125.34
name: Total reward
- type: expert_normalized_total_reward
value: 0.14 +/- 0.28
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: 6.87 +/- 44.99
name: Total reward
- type: expert_normalized_total_reward
value: 0.02 +/- 0.10
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: 264.18 +/- 195.69
name: Total reward
- type: expert_normalized_total_reward
value: 0.63 +/- 0.47
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: 697.54 +/- 137.79
name: Total reward
- type: expert_normalized_total_reward
value: 0.95 +/- 0.20
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: 196.80 +/- 1.73
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: 122.61 +/- 24.52
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: 497.42 +/- 168.74
name: Total reward
- type: expert_normalized_total_reward
value: 0.93 +/- 0.37
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: 91.41 +/- 115.05
name: Total reward
- type: expert_normalized_total_reward
value: 1.08 +/- 1.37
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: 116.49 +/- 208.05
name: Total reward
- type: expert_normalized_total_reward
value: 0.15 +/- 0.28
name: Expert normalized total reward
- task:
type: reinforcement-learning
name: Reinforcement Learning
dataset:
name: Push
type: metaworld-push
metrics:
- type: total_reward
value: 604.25 +/- 261.90
name: Total reward
- type: expert_normalized_total_reward
value: 0.80 +/- 0.35
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: 634.57 +/- 231.40
name: Total reward
- type: expert_normalized_total_reward
value: 0.81 +/- 0.38
name: Expert normalized total reward
- task:
type: reinforcement-learning
name: Reinforcement Learning
dataset:
name: Reach
type: metaworld-reach
metrics:
- type: total_reward
value: 325.27 +/- 159.21
name: Total reward
- type: expert_normalized_total_reward
value: 0.33 +/- 0.30
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: 124.60 +/- 112.83
name: Total reward
- type: expert_normalized_total_reward
value: 0.52 +/- 0.47
name: Expert normalized total reward
- task:
type: reinforcement-learning
name: Reinforcement Learning
dataset:
name: Soccer
type: metaworld-soccer
metrics:
- type: total_reward
value: 364.50 +/- 175.45
name: Total reward
- type: expert_normalized_total_reward
value: 0.97 +/- 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: 398.64 +/- 205.60
name: Total reward
- type: expert_normalized_total_reward
value: 0.76 +/- 0.39
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: 158.29 +/- 264.59
name: Total reward
- type: expert_normalized_total_reward
value: 0.25 +/- 0.42
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: 775.30 +/- 119.00
name: Total reward
- type: expert_normalized_total_reward
value: 0.97 +/- 0.15
name: Expert normalized total reward
- task:
type: reinforcement-learning
name: Reinforcement Learning
dataset:
name: Sweep
type: metaworld-sweep
metrics:
- type: total_reward
value: 15.64 +/- 9.29
name: Total reward
- type: expert_normalized_total_reward
value: 0.01 +/- 0.02
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: 423.33 +/- 203.92
name: Total reward
- type: expert_normalized_total_reward
value: 0.69 +/- 0.38
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: 593.10 +/- 54.83
name: Total reward
- type: expert_normalized_total_reward
value: 1.00 +/- 0.10
name: Expert normalized total reward
- task:
type: reinforcement-learning
name: Reinforcement Learning
dataset:
name: Ant
type: mujoco-ant
metrics:
- type: total_reward
value: 5268.02 +/- 1495.39
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: Inverted Double Pendulum
type: mujoco-doublependulum
metrics:
- type: total_reward
value: 4750.14 +/- 931.20
name: Total reward
- type: expert_normalized_total_reward
value: 0.51 +/- 0.10
name: Expert normalized total reward
- task:
type: reinforcement-learning
name: Reinforcement Learning
dataset:
name: Half Cheetah
type: mujoco-halfcheetah
metrics:
- type: total_reward
value: 6659.69 +/- 409.71
name: Total reward
- type: expert_normalized_total_reward
value: 0.90 +/- 0.05
name: Expert normalized total reward
- task:
type: reinforcement-learning
name: Reinforcement Learning
dataset:
name: Hopper
type: mujoco-hopper
metrics:
- type: total_reward
value: 1835.93 +/- 532.21
name: Total reward
- type: expert_normalized_total_reward
value: 0.99 +/- 0.29
name: Expert normalized total reward
- task:
type: reinforcement-learning
name: Reinforcement Learning
dataset:
name: Humanoid
type: mujoco-humanoid
metrics:
- type: total_reward
value: 697.44 +/- 108.06
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: 116.34 +/- 20.19
name: Total reward
- type: expert_normalized_total_reward
value: 0.23 +/- 0.04
name: Expert normalized total reward
- task:
type: reinforcement-learning
name: Reinforcement Learning
dataset:
name: Pusher
type: mujoco-pusher
metrics:
- type: total_reward
value: '-26.33 +/- 6.32'
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: Reacher
type: mujoco-reacher
metrics:
- type: total_reward
value: '-6.06 +/- 2.64'
name: Total reward
- type: expert_normalized_total_reward
value: 0.99 +/- 0.07
name: Expert normalized total reward
- task:
type: reinforcement-learning
name: Reinforcement Learning
dataset:
name: Humanoid Standup
type: mujoco-standup
metrics:
- type: total_reward
value: 118125.15 +/- 24880.28
name: Total reward
- type: expert_normalized_total_reward
value: 0.35 +/- 0.10
name: Expert normalized total reward
- task:
type: reinforcement-learning
name: Reinforcement Learning
dataset:
name: Swimmer
type: mujoco-swimmer
metrics:
- type: total_reward
value: 93.26 +/- 3.78
name: Total reward
- type: expert_normalized_total_reward
value: 1.01 +/- 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: 4662.43 +/- 762.67
name: Total reward
- type: expert_normalized_total_reward
value: 1.01 +/- 0.16
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: Coming soon
- Demo: Coming soon
Training
The model was trained on the following tasks:
- 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
How to Get Started with the Model
Use the code below to get started with the model.
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("jat-project/jat")