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	---
	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}
	}
	```