README.md

---
language:
- en
license: apache-2.0
size_categories:
- 10M<n<100M
task_categories:
- reinforcement-learning
pretty_name: Procgen Benchmark Dataset
dataset_info:
- config_name: bigfish
  features:
  - name: observation
    dtype:
      array3_d:
        shape:
        - 64
        - 64
        - 3
        dtype: uint8
  - name: action
    dtype: uint8
  - name: reward
    dtype: float32
  - name: done
    dtype: bool
  - name: truncated
    dtype: bool
  splits:
  - name: train
    num_bytes: 260435250000
    num_examples: 9000000
  - name: test
    num_bytes: 28937250000
    num_examples: 1000000
  download_size: 31592522500
  dataset_size: 289372500000
- config_name: bossfight
  features:
  - name: observation
    dtype:
      array3_d:
        shape:
        - 64
        - 64
        - 3
        dtype: uint8
  - name: action
    dtype: uint8
  - name: reward
    dtype: float32
  - name: done
    dtype: bool
  - name: truncated
    dtype: bool
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    num_bytes: 260435250000
    num_examples: 9000000
  - name: test
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    num_examples: 1000000
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- config_name: caveflyer
  features:
  - name: observation
    dtype:
      array3_d:
        shape:
        - 64
        - 64
        - 3
        dtype: uint8
  - name: action
    dtype: uint8
  - name: reward
    dtype: float32
  - name: done
    dtype: bool
  - name: truncated
    dtype: bool
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    num_examples: 9000000
  - name: test
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- config_name: chaser
  features:
  - name: observation
    dtype:
      array3_d:
        shape:
        - 64
        - 64
        - 3
        dtype: uint8
  - name: action
    dtype: uint8
  - name: reward
    dtype: float32
  - name: done
    dtype: bool
  - name: truncated
    dtype: bool
  splits:
  - name: train
    num_bytes: 26043525000
    num_examples: 900000
  - name: test
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    num_examples: 100000
  download_size: 2126890202
  dataset_size: 28937250000
- config_name: climber
  features:
  - name: observation
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      array3_d:
        shape:
        - 64
        - 64
        - 3
        dtype: uint8
  - name: action
    dtype: uint8
  - name: reward
    dtype: float32
  - name: done
    dtype: bool
  - name: truncated
    dtype: bool
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    num_bytes: 260435250000
    num_examples: 9000000
  - name: test
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    num_examples: 1000000
  download_size: 21907305642
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- config_name: coinrun
  features:
  - name: observation
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      array3_d:
        shape:
        - 64
        - 64
        - 3
        dtype: uint8
  - name: action
    dtype: uint8
  - name: reward
    dtype: float32
  - name: done
    dtype: bool
  - name: truncated
    dtype: bool
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  - name: train
    num_bytes: 260435250000
    num_examples: 9000000
  - name: test
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    num_examples: 1000000
  download_size: 25759185845
  dataset_size: 289372500000
- config_name: dodgeball
  features:
  - name: observation
    dtype:
      array3_d:
        shape:
        - 64
        - 64
        - 3
        dtype: uint8
  - name: action
    dtype: uint8
  - name: reward
    dtype: float32
  - name: done
    dtype: bool
  - name: truncated
    dtype: bool
  splits:
  - name: train
    num_bytes: 260435250000
    num_examples: 9000000
  - name: test
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    num_examples: 1000000
  download_size: 34038260004
  dataset_size: 289372500000
- config_name: fruitbot
  features:
  - name: observation
    dtype:
      array3_d:
        shape:
        - 64
        - 64
        - 3
        dtype: uint8
  - name: action
    dtype: uint8
  - name: reward
    dtype: float32
  - name: done
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  - name: truncated
    dtype: bool
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  - name: train
    num_bytes: 260435250000
    num_examples: 9000000
  - name: test
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    num_examples: 1000000
  download_size: 184877608931
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- config_name: heist
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  - name: observation
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      array3_d:
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        - 64
        - 64
        - 3
        dtype: uint8
  - name: action
    dtype: uint8
  - name: reward
    dtype: float32
  - name: done
    dtype: bool
  - name: truncated
    dtype: bool
  splits:
  - name: train
    num_bytes: 260435250000
    num_examples: 9000000
  - name: test
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    num_examples: 1000000
  download_size: 25378574656
  dataset_size: 289372500000
- config_name: jumper
  features:
  - name: observation
    dtype:
      array3_d:
        shape:
        - 64
        - 64
        - 3
        dtype: uint8
  - name: action
    dtype: uint8
  - name: reward
    dtype: float32
  - name: done
    dtype: bool
  - name: truncated
    dtype: bool
  splits:
  - name: train
    num_bytes: 260435250000
    num_examples: 9000000
  - name: test
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    num_examples: 1000000
  download_size: 32385518771
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- config_name: leaper
  features:
  - name: observation
    dtype:
      array3_d:
        shape:
        - 64
        - 64
        - 3
        dtype: uint8
  - name: action
    dtype: uint8
  - name: reward
    dtype: float32
  - name: done
    dtype: bool
  - name: truncated
    dtype: bool
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    num_bytes: 26043525000
    num_examples: 900000
  - name: test
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    num_examples: 100000
  download_size: 2281835608
  dataset_size: 28937250000
- config_name: maze
  features:
  - name: observation
    dtype:
      array3_d:
        shape:
        - 64
        - 64
        - 3
        dtype: uint8
  - name: action
    dtype: uint8
  - name: reward
    dtype: float32
  - name: done
    dtype: bool
  - name: truncated
    dtype: bool
  splits:
  - name: train
    num_bytes: 26043525000
    num_examples: 900000
  - name: test
    num_bytes: 2893725000
    num_examples: 100000
  download_size: 2458751741
  dataset_size: 28937250000
- config_name: miner
  features:
  - name: observation
    dtype:
      array3_d:
        shape:
        - 64
        - 64
        - 3
        dtype: uint8
  - name: action
    dtype: uint8
  - name: reward
    dtype: float32
  - name: done
    dtype: bool
  - name: truncated
    dtype: bool
  splits:
  - name: train
    num_bytes: 260435250000
    num_examples: 9000000
  - name: test
    num_bytes: 28937250000
    num_examples: 1000000
  download_size: 18949118303
  dataset_size: 289372500000
- config_name: ninja
  features:
  - name: observation
    dtype:
      array3_d:
        shape:
        - 64
        - 64
        - 3
        dtype: uint8
  - name: action
    dtype: uint8
  - name: reward
    dtype: float32
  - name: done
    dtype: bool
  - name: truncated
    dtype: bool
  splits:
  - name: train
    num_bytes: 260435250000
    num_examples: 9000000
  - name: test
    num_bytes: 28937250000
    num_examples: 1000000
  download_size: 33644711360
  dataset_size: 289372500000
- config_name: plunder
  features:
  - name: observation
    dtype:
      array3_d:
        shape:
        - 64
        - 64
        - 3
        dtype: uint8
  - name: action
    dtype: uint8
  - name: reward
    dtype: float32
  - name: done
    dtype: bool
  - name: truncated
    dtype: bool
  splits:
  - name: test
    num_bytes: 2893725000
    num_examples: 100000
  - name: train
    num_bytes: 260435250000
    num_examples: 9000000
  download_size: 31185607723
  dataset_size: 263328975000
- config_name: starpilot
  features:
  - name: observation
    dtype:
      array3_d:
        shape:
        - 64
        - 64
        - 3
        dtype: uint8
  - name: action
    dtype: uint8
  - name: reward
    dtype: float32
  - name: done
    dtype: bool
  - name: truncated
    dtype: bool
  splits:
  - name: train
    num_bytes: 260435250000
    num_examples: 9000000
  - name: test
    num_bytes: 28937250000
    num_examples: 1000000
  download_size: 86584615866
  dataset_size: 289372500000
configs:
- config_name: bigfish
  data_files:
  - split: train
    path: bigfish/train-*
  - split: test
    path: bigfish/test-*
- config_name: bossfight
  data_files:
  - split: train
    path: bossfight/train-*
  - split: test
    path: bossfight/test-*
- config_name: caveflyer
  data_files:
  - split: train
    path: caveflyer/train-*
  - split: test
    path: caveflyer/test-*
- config_name: chaser
  data_files:
  - split: train
    path: chaser/train-*
  - split: test
    path: chaser/test-*
- config_name: climber
  data_files:
  - split: train
    path: climber/train-*
  - split: test
    path: climber/test-*
- config_name: coinrun
  data_files:
  - split: train
    path: coinrun/train-*
  - split: test
    path: coinrun/test-*
- config_name: dodgeball
  data_files:
  - split: train
    path: dodgeball/train-*
  - split: test
    path: dodgeball/test-*
- config_name: fruitbot
  data_files:
  - split: train
    path: fruitbot/train-*
  - split: test
    path: fruitbot/test-*
- config_name: heist
  data_files:
  - split: train
    path: heist/train-*
  - split: test
    path: heist/test-*
- config_name: jumper
  data_files:
  - split: train
    path: jumper/train-*
  - split: test
    path: jumper/test-*
- config_name: leaper
  data_files:
  - split: train
    path: leaper/train-*
  - split: test
    path: leaper/test-*
- config_name: maze
  data_files:
  - split: train
    path: maze/train-*
  - split: test
    path: maze/test-*
- config_name: miner
  data_files:
  - split: train
    path: miner/train-*
  - split: test
    path: miner/test-*
- config_name: ninja
  data_files:
  - split: train
    path: ninja/train-*
  - split: test
    path: ninja/test-*
- config_name: plunder
  data_files:
  - split: train
    path: plunder/train-*
  - split: test
    path: plunder/test-*
- config_name: starpilot
  data_files:
  - split: train
    path: starpilot/train-*
  - split: test
    path: starpilot/test-*
tags:
- procgen
- bigfish
- benchmark
- openai
- bossfight
- caveflyer
- chaser
- climber
- dodgeball
- fruitbot
- heist
- jumper
- leaper
- maze
- miner
- ninja
- plunder
- starpilot
---
# Procgen Benchmark
This dataset contains expert trajectories generated by a [PPO](https://arxiv.org/abs/1707.06347) reinforcement learning agent trained on each of the 16 procedurally-generated gym environments from the [Procgen Benchmark](https://openai.com/index/procgen-benchmark/). The environments were created on `distribution_mode=easy` and with unlimited levels.

Disclaimer: This is not an official repository from OpenAI.

## Dataset Usage

Regular usage (for environment bigfish):
```python
from datasets import load_dataset
train_dataset = load_dataset("EpicPinkPenguin/procgen", name="bigfish", split="train")
test_dataset = load_dataset("EpicPinkPenguin/procgen", name="bigfish", split="test")
```

Usage with PyTorch (for environment bossfight):
```python
from datasets import load_dataset
train_dataset = load_dataset("EpicPinkPenguin/procgen", name="bossfight", split="train").with_format("torch")
test_dataset = load_dataset("EpicPinkPenguin/procgen", name="bossfight", split="test").with_format("torch")
```

## Agent Performance
The PPO RL agent was trained for 50M steps on each environment and obtained the following final performance metrics.

| Environment | Steps (Train) | Steps (Test) | Return | Observation |
|:------------|:--------------|:-------------|:-------|:------------|
| bigfish     | 900,000       | 100.000      | 29.16  | <video controls autoplay loop src="https://cdn-uploads.huggingface.co/production/uploads/633c1daf31c06121a58f2df9/lHQXBqLdoWicXlt68I9QX.mp4"></video>  |
| bossfight   | 900,000       | 100.000      | 11.35  | <video controls autoplay loop src="https://cdn-uploads.huggingface.co/production/uploads/633c1daf31c06121a58f2df9/LPoafGi4YBWqqkuFlEN_l.mp4"></video>  |
| caveflyer   | 900,000       | 100.000      | 09.47  | <video controls autoplay loop src="https://cdn-uploads.huggingface.co/production/uploads/633c1daf31c06121a58f2df9/XVqRwu_9yfX4ECQc4At4G.mp4"></video>  |
| chaser      | 900,000       | 100.000      | 11.46  | <video controls autoplay loop src="https://cdn-uploads.huggingface.co/production/uploads/633c1daf31c06121a58f2df9/FIKVv48SThqiC1Z2PYQ7U.mp4"></video>  |
| climber     | 900,000       | 100.000      | 11.17  | <video controls autoplay loop src="https://cdn-uploads.huggingface.co/production/uploads/633c1daf31c06121a58f2df9/XJQlA7IyF9_gwUiw-FkND.mp4"></video>  |
| coinrun     | 900,000       | 100.000      | 09.74  | <video controls autoplay loop src="https://cdn-uploads.huggingface.co/production/uploads/633c1daf31c06121a58f2df9/Ucv3HZttewMRQzTL8r_Tw.mp4"></video>  |
| dodgeball   | 900,000       | 100.000      | 16.78  | <video controls autoplay loop src="https://cdn-uploads.huggingface.co/production/uploads/633c1daf31c06121a58f2df9/5HetbKuXBpO-v1jcVyLTU.mp4"></video>  |
| fruitbot    | 900,000       | 100.000      | 29.87  | <video controls autoplay loop src="https://cdn-uploads.huggingface.co/production/uploads/633c1daf31c06121a58f2df9/zKCyxXvauXjUac-5kEAWz.mp4"></video>  |
| heist       | 900,000       | 100.000      | 09.98  | <video controls autoplay loop src="https://cdn-uploads.huggingface.co/production/uploads/633c1daf31c06121a58f2df9/AdZ6XNmUN5_00BKd9BN8R.mp4"></video>  |
| jumper      | 900,000       | 100.000      | 08.71  | <video controls autoplay loop src="https://cdn-uploads.huggingface.co/production/uploads/633c1daf31c06121a58f2df9/s5k31gWK2Vc6Lp6QVzQXA.mp4"></video>  |
| leaper      | 900,000       | 100.000      | 07.71  | <video controls autoplay loop src="https://cdn-uploads.huggingface.co/production/uploads/633c1daf31c06121a58f2df9/_hDMocxjmzutc0t5FfoTX.mp4"></video>  |
| maze        | 900,000       | 100.000      | 09.99  | <video controls autoplay loop src="https://cdn-uploads.huggingface.co/production/uploads/633c1daf31c06121a58f2df9/uhNdDPuNhZpxVns91Ba-9.mp4"></video>  |
| miner       | 900,000       | 100.000      | 12.63  | <video controls autoplay loop src="https://cdn-uploads.huggingface.co/production/uploads/633c1daf31c06121a58f2df9/ElpJ8l2WHJGrprZ3-giHU.mp4"></video>  |
| ninja       | 900,000       | 100.000      | 09.44  | <video controls autoplay loop src="https://cdn-uploads.huggingface.co/production/uploads/633c1daf31c06121a58f2df9/b9i-fb2Twh8XmBBNf2DRG.mp4"></video>  |
| plunder     | 900,000       | 100.000      | 25.98  | <video controls autoplay loop src="https://cdn-uploads.huggingface.co/production/uploads/633c1daf31c06121a58f2df9/JPeGNOVzrotuYUjfzZj40.mp4"></video>  |
| starpilot   | 900,000       | 100.000      | 55.28  | <video controls autoplay loop src="https://cdn-uploads.huggingface.co/production/uploads/633c1daf31c06121a58f2df9/wY9lZgkw5tor19hCWmm6A.mp4"></video>  |


## Dataset Structure
### Data Instances
Each data instance represents a single step consisting of tuples of the form (observation, action, reward, done, truncated) = (o_t, a_t, r_{t+1}, done_{t+1}, trunc_{t+1}).

```json
{'action': 1,
 'done': False,
 'observation': [[[0, 166, 253],
                  [0, 174, 255],
                  [0, 170, 251],
                  [0, 191, 255],
                  [0, 191, 255],
                  [0, 221, 255],
                  [0, 243, 255],
                  [0, 248, 255],
                  [0, 243, 255],
                  [10, 239, 255],
                  [25, 255, 255],
                  [0, 241, 255],
                  [0, 235, 255],
                  [17, 240, 255],
                  [10, 243, 255],
                  [27, 253, 255],
                  [39, 255, 255],
                  [58, 255, 255],
                  [85, 255, 255],
                  [111, 255, 255],
                  [135, 255, 255],
                  [151, 255, 255],
                  [173, 255, 255],
...
                  [0, 0, 37],
                  [0, 0, 39]]],
 'reward': 0.0,
 'truncated': False}
```

### Data Fields
- `observation`: The current RGB observation from the environment.
- `action`: The action predicted by the agent for the current observation.
- `reward`: The received reward from stepping the environment with the current action.
- `done`: If the new observation is the start of a new episode. Obtained after stepping the environment with the current action.
- `truncated`: If the new observation is the start of a new episode due to truncation. Obtained after stepping the environment with the current action.

### Data Splits
The dataset is divided into a `train` (90%) and `test` (10%) split. Each environment-dataset has in sum 1M steps (data points).

## Dataset Creation
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.

## Procgen Benchmark
The [Procgen Benchmark](https://openai.com/index/procgen-benchmark/), released by OpenAI, consists of 16 procedurally-generated environments designed to measure how quickly reinforcement learning (RL) agents learn generalizable skills. It emphasizes experimental convenience, high diversity within and across environments, and is ideal for evaluating both sample efficiency and generalization. The benchmark allows for distinct training and test sets in each environment, making it a standard research platform for the OpenAI RL team. It aims to address the need for more diverse RL benchmarks compared to complex environments like Dota and StarCraft.