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README.md
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@@ -581,24 +581,24 @@ test_dataset = load_dataset("EpicPinkPenguin/procgen", name="bossfight", split="
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## Agent Performance
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The PPO RL agent was trained for 50M steps on each environment and obtained the following final performance metrics.
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| Environment |
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| bigfish |
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| 587 |
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| bossfight |
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| caveflyer |
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| 589 |
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| chaser |
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| 590 |
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| climber |
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| 591 |
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| coinrun |
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| 592 |
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| dodgeball |
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| 593 |
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| fruitbot |
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| heist |
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| jumper |
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| leaper |
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| 597 |
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| maze |
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| 598 |
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| miner |
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| 599 |
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| ninja |
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| 600 |
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| plunder |
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| starpilot |
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## Dataset Structure
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@@ -646,7 +646,7 @@ Each data instance represents a single step consisting of tuples of the form (ob
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- `truncated`: If the new observation is the start of a new episode due to truncation. Obtained after stepping the environment with the current action.
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### Data Splits
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The dataset is divided into a `train` (90%) and `test` (10%) split. Each environment-dataset has in sum
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## Dataset Creation
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The dataset was created by training an RL agent with [PPO](https://arxiv.org/abs/1707.06347) for 50M steps in each environment. The trajectories where generated by sampling from the predicted action distribution at each step (not taking the argmax). The environments were created on `distribution_mode=easy` and with unlimited levels.
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| 581 |
## Agent Performance
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| 582 |
The PPO RL agent was trained for 50M steps on each environment and obtained the following final performance metrics.
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| 583 |
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| 584 |
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| Environment | Steps (Train) | Steps (Test) | Return | Observation |
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|:------------|:----------------|:---------------|:-------|:------------|
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| bigfish | 9,000,000 | 1,000,000 | 29.38 | <video controls autoplay loop src="https://cdn-uploads.huggingface.co/production/uploads/633c1daf31c06121a58f2df9/lHQXBqLdoWicXlt68I9QX.mp4"></video> |
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| 587 |
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| bossfight | 9,000,000 | 1,000,000 | 10.86 | <video controls autoplay loop src="https://cdn-uploads.huggingface.co/production/uploads/633c1daf31c06121a58f2df9/LPoafGi4YBWqqkuFlEN_l.mp4"></video> |
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| caveflyer | 9,000,000 | 1,000,000 | 09.58 | <video controls autoplay loop src="https://cdn-uploads.huggingface.co/production/uploads/633c1daf31c06121a58f2df9/XVqRwu_9yfX4ECQc4At4G.mp4"></video> |
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| chaser | 9,000,000 | 1,000,000 | 11.52 | <video controls autoplay loop src="https://cdn-uploads.huggingface.co/production/uploads/633c1daf31c06121a58f2df9/FIKVv48SThqiC1Z2PYQ7U.mp4"></video> |
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| 590 |
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| climber | 9,000,000 | 1,000,000 | 10.66 | <video controls autoplay loop src="https://cdn-uploads.huggingface.co/production/uploads/633c1daf31c06121a58f2df9/XJQlA7IyF9_gwUiw-FkND.mp4"></video> |
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| coinrun | 9,000,000 | 1,000,000 | 09.84 | <video controls autoplay loop src="https://cdn-uploads.huggingface.co/production/uploads/633c1daf31c06121a58f2df9/Ucv3HZttewMRQzTL8r_Tw.mp4"></video> |
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| dodgeball | 9,000,000 | 1,000,000 | 16.73 | <video controls autoplay loop src="https://cdn-uploads.huggingface.co/production/uploads/633c1daf31c06121a58f2df9/5HetbKuXBpO-v1jcVyLTU.mp4"></video> |
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| fruitbot | 9,000,000 | 1,000,000 | 21.39 | <video controls autoplay loop src="https://cdn-uploads.huggingface.co/production/uploads/633c1daf31c06121a58f2df9/zKCyxXvauXjUac-5kEAWz.mp4"></video> |
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| 594 |
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| heist | 9,000,000 | 1,000,000 | 09.95 | <video controls autoplay loop src="https://cdn-uploads.huggingface.co/production/uploads/633c1daf31c06121a58f2df9/AdZ6XNmUN5_00BKd9BN8R.mp4"></video> |
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| jumper | 9,000,000 | 1,000,000 | 08.76 | <video controls autoplay loop src="https://cdn-uploads.huggingface.co/production/uploads/633c1daf31c06121a58f2df9/s5k31gWK2Vc6Lp6QVzQXA.mp4"></video> |
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| leaper | 9,000,000 | 1,000,000 | 07.71 | <video controls autoplay loop src="https://cdn-uploads.huggingface.co/production/uploads/633c1daf31c06121a58f2df9/_hDMocxjmzutc0t5FfoTX.mp4"></video> |
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| maze | 9,000,000 | 1,000,000 | 09.99 | <video controls autoplay loop src="https://cdn-uploads.huggingface.co/production/uploads/633c1daf31c06121a58f2df9/uhNdDPuNhZpxVns91Ba-9.mp4"></video> |
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| miner | 9,000,000 | 1,000,000 | 12.66 | <video controls autoplay loop src="https://cdn-uploads.huggingface.co/production/uploads/633c1daf31c06121a58f2df9/ElpJ8l2WHJGrprZ3-giHU.mp4"></video> |
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| ninja | 9,000,000 | 1,000,000 | 09.61 | <video controls autoplay loop src="https://cdn-uploads.huggingface.co/production/uploads/633c1daf31c06121a58f2df9/b9i-fb2Twh8XmBBNf2DRG.mp4"></video> |
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| plunder | 9,000,000 | 1,000,000 | 25.68 | <video controls autoplay loop src="https://cdn-uploads.huggingface.co/production/uploads/633c1daf31c06121a58f2df9/JPeGNOVzrotuYUjfzZj40.mp4"></video> |
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| starpilot | 9,000,000 | 1,000,000 | 57.25 | <video controls autoplay loop src="https://cdn-uploads.huggingface.co/production/uploads/633c1daf31c06121a58f2df9/wY9lZgkw5tor19hCWmm6A.mp4"></video> |
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| 602 |
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| 603 |
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| 604 |
## Dataset Structure
|
|
|
|
| 646 |
- `truncated`: If the new observation is the start of a new episode due to truncation. Obtained after stepping the environment with the current action.
|
| 647 |
|
| 648 |
### Data Splits
|
| 649 |
+
The dataset is divided into a `train` (90%) and `test` (10%) split. Each environment-dataset has in sum 10M steps (data points).
|
| 650 |
|
| 651 |
## Dataset Creation
|
| 652 |
The dataset was created by training an RL agent with [PPO](https://arxiv.org/abs/1707.06347) for 50M steps in each environment. The trajectories where generated by sampling from the predicted action distribution at each step (not taking the argmax). The environments were created on `distribution_mode=easy` and with unlimited levels.
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