
# 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")
```


{"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": "0.14 [0.14, 0.15]", "name": "IQM expert normalized total reward"}, {"type": "iqm_human_normalized_total_reward", "value": "0.38 [0.37, 0.38]", "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": "1474.90 +/- 588.75", "name": "Total reward"}, {"type": "expert_normalized_total_reward", "value": "0.07 +/- 0.04", "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": "Amidar", "type": "atari-amidar"}, "metrics": [{"type": "total_reward", "value": "104.89 +/- 103.52", "name": "Total reward"}, {"type": "expert_normalized_total_reward", "value": "0.05 +/- 0.05", "name": "Expert normalized total reward"}, {"type": "human_normalized_total_reward", "value": "0.06 +/- 0.06", "name": "Human normalized total reward"}]}, {"task": {"type": "reinforcement-learning", "name": "Reinforcement Learning"}, "dataset": {"name": "Assault", "type": "atari-assault"}, "metrics": [{"type": "total_reward", "value": "1650.07 +/- 820.99", "name": "Total reward"}, {"type": "expert_normalized_total_reward", "value": "0.09 +/- 0.05", "name": "Expert normalized total reward"}, {"type": "human_normalized_total_reward", "value": "2.75 +/- 1.58", "name": "Human normalized total reward"}]}, {"task": {"type": "reinforcement-learning", "name": "Reinforcement Learning"}, "dataset": {"name": "Asterix", "type": "atari-asterix"}, "metrics": [{"type": "total_reward", "value": "800.00 +/- 584.85", "name": "Total reward"}, {"type": "expert_normalized_total_reward", "value": "0.17 +/- 0.17", "name": "Expert normalized total reward"}, {"type": "human_normalized_total_reward", "value": "0.07 +/- 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": "1385.30 +/- 507.53", "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": "66980.00 +/- 158449.73", "name": "Total reward"}, {"type": "expert_normalized_total_reward", "value": "0.18 +/- 0.51", "name": "Expert normalized total reward"}, {"type": "human_normalized_total_reward", "value": "3.35 +/- 9.79", "name": "Human normalized total reward"}]}, {"task": {"type": "reinforcement-learning", "name": "Reinforcement Learning"}, "dataset": {"name": "Bank Heist", "type": "atari-bankheist"}, "metrics": [{"type": "total_reward", "value": "948.30 +/- 199.86", "name": "Total reward"}, {"type": "expert_normalized_total_reward", "value": "0.71 +/- 0.15", "name": "Expert normalized total reward"}, {"type": "human_normalized_total_reward", "value": "1.26 +/- 0.27", "name": "Human normalized total reward"}]}, {"task": {"type": "reinforcement-learning", "name": "Reinforcement Learning"}, "dataset": {"name": "Battle Zone", "type": "atari-battlezone"}, "metrics": [{"type": "total_reward", "value": "17420.00 +/- 6071.54", "name": "Total reward"}, {"type": "expert_normalized_total_reward", "value": "0.06 +/- 0.02", "name": "Expert normalized total reward"}, {"type": "human_normalized_total_reward", "value": "0.47 +/- 0.16", "name": "Human normalized total reward"}]}, {"task": {"type": "reinforcement-learning", "name": "Reinforcement Learning"}, "dataset": {"name": "Beam Rider", "type": "atari-beamrider"}, "metrics": [{"type": "total_reward", "value": "797.32 +/- 328.31", "name": "Total reward"}, {"type": "expert_normalized_total_reward", "value": "0.01 +/- 0.01", "name": "Expert normalized total reward"}, {"type": "human_normalized_total_reward", "value": "0.03 +/- 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": "687.30 +/- 331.91", "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 +/- 0.13", "name": "Human normalized total reward"}]}, {"task": {"type": "reinforcement-learning", "name": "Reinforcement Learning"}, "dataset": {"name": "Bowling", "type": "atari-bowling"}, "metrics": [{"type": "total_reward", "value": "22.41 +/- 5.57", "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": "90.10 +/- 23.05", "name": "Total reward"}, {"type": "expert_normalized_total_reward", "value": "0.92 +/- 0.24", "name": "Expert normalized total reward"}, {"type": "human_normalized_total_reward", "value": "7.50 +/- 1.92", "name": "Human normalized total reward"}]}, {"task": {"type": "reinforcement-learning", "name": "Reinforcement Learning"}, "dataset": {"name": "Breakout", "type": "atari-breakout"}, "metrics": [{"type": "total_reward", "value": "8.82 +/- 5.63", "name": "Total reward"}, {"type": "expert_normalized_total_reward", "value": "0.01 +/- 0.01", "name": "Expert normalized total reward"}, {"type": "human_normalized_total_reward", "value": "0.25 +/- 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": "5589.92 +/- 2567.26", "name": "Total reward"}, {"type": "expert_normalized_total_reward", "value": "0.37 +/- 0.27", "name": "Expert normalized total reward"}, {"type": "human_normalized_total_reward", "value": "0.35 +/- 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": "2417.00 +/- 1489.90", "name": "Total reward"}, {"type": "expert_normalized_total_reward", "value": "0.02 +/- 0.02", "name": "Expert normalized total reward"}, {"type": "human_normalized_total_reward", "value": "0.24 +/- 0.23", "name": "Human normalized total reward"}]}, {"task": {"type": "reinforcement-learning", "name": "Reinforcement Learning"}, "dataset": {"name": "Crazy Climber", "type": "atari-crazyclimber"}, "metrics": [{"type": "total_reward", "value": "97639.00 +/- 26184.68", "name": "Total reward"}, {"type": "expert_normalized_total_reward", "value": "0.52 +/- 0.16", "name": "Expert normalized total reward"}, {"type": "human_normalized_total_reward", "value": "3.47 +/- 1.05", "name": "Human normalized total reward"}]}, {"task": {"type": "reinforcement-learning", "name": "Reinforcement Learning"}, "dataset": {"name": "Defender", "type": "atari-defender"}, "metrics": [{"type": "total_reward", "value": "39323.50 +/- 15202.98", "name": "Total reward"}, {"type": "expert_normalized_total_reward", "value": "0.10 +/- 0.04", "name": "Expert normalized total reward"}, {"type": "human_normalized_total_reward", "value": "2.30 +/- 0.96", "name": "Human normalized total reward"}]}, {"task": {"type": "reinforcement-learning", "name": "Reinforcement Learning"}, "dataset": {"name": "Demon Attack", "type": "atari-demonattack"}, "metrics": [{"type": "total_reward", "value": "815.30 +/- 989.67", "name": "Total reward"}, {"type": "expert_normalized_total_reward", "value": "0.01 +/- 0.01", "name": "Expert normalized total reward"}, {"type": "human_normalized_total_reward", "value": "0.36 +/- 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": "14.42 +/- 9.97", "name": "Total reward"}, {"type": "expert_normalized_total_reward", "value": "0.84 +/- 0.25", "name": "Expert normalized total reward"}, {"type": "human_normalized_total_reward", "value": "0.94 +/- 0.28", "name": "Human normalized total reward"}]}, {"task": {"type": "reinforcement-learning", "name": "Reinforcement Learning"}, "dataset": {"name": "Enduro", "type": "atari-enduro"}, "metrics": [{"type": "total_reward", "value": "108.52 +/- 42.73", "name": "Total reward"}, {"type": "expert_normalized_total_reward", "value": "0.05 +/- 0.02", "name": "Expert normalized total reward"}, {"type": "human_normalized_total_reward", "value": "0.13 +/- 0.05", "name": "Human normalized total reward"}]}, {"task": {"type": "reinforcement-learning", "name": "Reinforcement Learning"}, "dataset": {"name": "Fishing Derby", "type": "atari-fishingderby"}, "metrics": [{"type": "total_reward", "value": "-30.35 +/- 24.37", "name": "Total reward"}, {"type": "expert_normalized_total_reward", "value": "0.62 +/- 0.25", "name": "Expert normalized total reward"}, {"type": "human_normalized_total_reward", "value": "0.47 +/- 0.19", "name": "Human normalized total reward"}]}, {"task": {"type": "reinforcement-learning", "name": "Reinforcement Learning"}, "dataset": {"name": "Freeway", "type": "atari-freeway"}, "metrics": [{"type": "total_reward", "value": "27.49 +/- 1.63", "name": "Total reward"}, {"type": "expert_normalized_total_reward", "value": "0.81 +/- 0.05", "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": "2769.60 +/- 1445.61", "name": "Total reward"}, {"type": "expert_normalized_total_reward", "value": "0.21 +/- 0.11", "name": "Expert normalized total reward"}, {"type": "human_normalized_total_reward", "value": "0.63 +/- 0.34", "name": "Human normalized total reward"}]}, {"task": {"type": "reinforcement-learning", "name": "Reinforcement Learning"}, "dataset": {"name": "Gopher", "type": "atari-gopher"}, "metrics": [{"type": "total_reward", "value": "5340.60 +/- 2547.07", "name": "Total reward"}, {"type": "expert_normalized_total_reward", "value": "0.06 +/- 0.03", "name": "Expert normalized total reward"}, {"type": "human_normalized_total_reward", "value": "2.36 +/- 1.18", "name": "Human normalized total reward"}]}, {"task": {"type": "reinforcement-learning", "name": "Reinforcement Learning"}, "dataset": {"name": "Gravitar", "type": "atari-gravitar"}, "metrics": [{"type": "total_reward", "value": "1269.50 +/- 902.99", "name": "Total reward"}, {"type": "expert_normalized_total_reward", "value": "0.29 +/- 0.24", "name": "Expert normalized total reward"}, {"type": "human_normalized_total_reward", "value": "0.34 +/- 0.28", "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": "11709.65 +/- 3233.53", "name": "Total reward"}, {"type": "expert_normalized_total_reward", "value": "0.24 +/- 0.07", "name": "Expert normalized total reward"}, {"type": "human_normalized_total_reward", "value": "0.36 +/- 0.11", "name": "Human normalized total reward"}]}, {"task": {"type": "reinforcement-learning", "name": "Reinforcement Learning"}, "dataset": {"name": "Ice Hockey", "type": "atari-icehockey"}, "metrics": [{"type": "total_reward", "value": "7.48 +/- 5.60", "name": "Total reward"}, {"type": "expert_normalized_total_reward", "value": "0.51 +/- 0.15", "name": "Expert normalized total reward"}, {"type": "human_normalized_total_reward", "value": "1.54 +/- 0.46", "name": "Human normalized total reward"}]}, {"task": {"type": "reinforcement-learning", "name": "Reinforcement Learning"}, "dataset": {"name": "James Bond", "type": "atari-jamesbond"}, "metrics": [{"type": "total_reward", "value": "327.50 +/- 123.16", "name": "Total reward"}, {"type": "expert_normalized_total_reward", "value": "0.01 +/- 0.00", "name": "Expert normalized total reward"}, {"type": "human_normalized_total_reward", "value": "1.09 +/- 0.45", "name": "Human normalized total reward"}]}, {"task": {"type": "reinforcement-learning", "name": "Reinforcement Learning"}, "dataset": {"name": "Kangaroo", "type": "atari-kangaroo"}, "metrics": [{"type": "total_reward", "value": "378.00 +/- 343.97", "name": "Total reward"}, {"type": "expert_normalized_total_reward", "value": "0.62 +/- 0.66", "name": "Expert normalized total reward"}, {"type": "human_normalized_total_reward", "value": "0.11 +/- 0.12", "name": "Human normalized total reward"}]}, {"task": {"type": "reinforcement-learning", "name": "Reinforcement Learning"}, "dataset": {"name": "Krull", "type": "atari-krull"}, "metrics": [{"type": "total_reward", "value": "10720.50 +/- 1284.13", "name": "Total reward"}, {"type": "expert_normalized_total_reward", "value": "0.93 +/- 0.13", "name": "Expert normalized total reward"}, {"type": "human_normalized_total_reward", "value": "8.55 +/- 1.20", "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": "288.00 +/- 255.06", "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": "1573.10 +/- 483.96", "name": "Total reward"}, {"type": "expert_normalized_total_reward", "value": "0.19 +/- 0.07", "name": "Expert normalized total reward"}, {"type": "human_normalized_total_reward", "value": "0.19 +/- 0.07", "name": "Human normalized total reward"}]}, {"task": {"type": 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"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"}]}]}]}
transformers safetensors jat text-generation 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 custom_code dataset:jat-project/jat-dataset arxiv:2402.09844 model-index autotrain_compatible region:us