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: 129932068797
  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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  - name: train
    num_bytes: 260435250000
    num_examples: 9000000
  - name: test
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    num_examples: 1000000
  download_size: 198057598671
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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: 260435250000
    num_examples: 9000000
  - name: test
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    num_examples: 1000000
  download_size: 42555126877
  dataset_size: 289372500000
- config_name: climber
  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
  download_size: 42586772888
  dataset_size: 289372500000
- config_name: coinrun
  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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  - name: train
    num_bytes: 260435250000
    num_examples: 9000000
  - name: test
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    num_examples: 1000000
  download_size: 51408569531
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- 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: 69558620534
  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
    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: 271549939959
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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: 74316944819
  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
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  - name: train
    num_bytes: 260435250000
    num_examples: 9000000
  - name: test
    num_bytes: 28937250000
    num_examples: 1000000
  download_size: 101573987650
  dataset_size: 289372500000
- 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_examples: 1000000
  - name: train
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  download_size: 64559698696
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- 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: 260435250000
    num_examples: 9000000
  - name: test
    num_bytes: 28937250000
    num_examples: 1000000
  download_size: 50930919120
  dataset_size: 289372500000
- 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: 38184188600
  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: 66644794325
  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: train
    num_bytes: 260435250000
    num_examples: 9000000
  - name: test
    num_bytes: 28937250000
    num_examples: 1000000
  download_size: 68795928729
  dataset_size: 289372500000
- 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: 170031712117
  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     | 9,000,000       | 1,000,000      | 29.72  | <video controls autoplay loop src="https://cdn-uploads.huggingface.co/production/uploads/633c1daf31c06121a58f2df9/lHQXBqLdoWicXlt68I9QX.mp4"></video>  |
| bossfight   | 9,000,000       | 1,000,000      | 11.13  | <video controls autoplay loop src="https://cdn-uploads.huggingface.co/production/uploads/633c1daf31c06121a58f2df9/LPoafGi4YBWqqkuFlEN_l.mp4"></video>  |
| caveflyer   | 9,000,000       | 1,000,000      | 08.95  | <video controls autoplay loop src="https://cdn-uploads.huggingface.co/production/uploads/633c1daf31c06121a58f2df9/XVqRwu_9yfX4ECQc4At4G.mp4"></video>  |
| chaser      | 9,000,000       | 1,000,000      | 10.98  | <video controls autoplay loop src="https://cdn-uploads.huggingface.co/production/uploads/633c1daf31c06121a58f2df9/FIKVv48SThqiC1Z2PYQ7U.mp4"></video>  |
| climber     | 9,000,000       | 1,000,000      | 11.66  | <video controls autoplay loop src="https://cdn-uploads.huggingface.co/production/uploads/633c1daf31c06121a58f2df9/XJQlA7IyF9_gwUiw-FkND.mp4"></video>  |
| coinrun     | 9,000,000       | 1,000,000      | 09.61  | <video controls autoplay loop src="https://cdn-uploads.huggingface.co/production/uploads/633c1daf31c06121a58f2df9/Ucv3HZttewMRQzTL8r_Tw.mp4"></video>  |
| dodgeball   | 9,000,000       | 1,000,000      | 11.07  | <video controls autoplay loop src="https://cdn-uploads.huggingface.co/production/uploads/633c1daf31c06121a58f2df9/5HetbKuXBpO-v1jcVyLTU.mp4"></video>  |
| fruitbot    | 9,000,000       | 1,000,000      | 32.49  | <video controls autoplay loop src="https://cdn-uploads.huggingface.co/production/uploads/633c1daf31c06121a58f2df9/zKCyxXvauXjUac-5kEAWz.mp4"></video>  |
| heist       | 9,000,000       | 1,000,000      | 08.37  | <video controls autoplay loop src="https://cdn-uploads.huggingface.co/production/uploads/633c1daf31c06121a58f2df9/AdZ6XNmUN5_00BKd9BN8R.mp4"></video>  |
| jumper      | 9,000,000       | 1,000,000      | 08.46  | <video controls autoplay loop src="https://cdn-uploads.huggingface.co/production/uploads/633c1daf31c06121a58f2df9/s5k31gWK2Vc6Lp6QVzQXA.mp4"></video>  |
| leaper      | 9,000,000       | 1,000,000      | 07.11  | <video controls autoplay loop src="https://cdn-uploads.huggingface.co/production/uploads/633c1daf31c06121a58f2df9/_hDMocxjmzutc0t5FfoTX.mp4"></video>  |
| maze        | 9,000,000       | 1,000,000      | 09.95  | <video controls autoplay loop src="https://cdn-uploads.huggingface.co/production/uploads/633c1daf31c06121a58f2df9/uhNdDPuNhZpxVns91Ba-9.mp4"></video>  |
| miner       | 9,000,000       | 1,000,000      | 12.21  | <video controls autoplay loop src="https://cdn-uploads.huggingface.co/production/uploads/633c1daf31c06121a58f2df9/ElpJ8l2WHJGrprZ3-giHU.mp4"></video>  |
| ninja       | 9,000,000       | 1,000,000      | 08.88  | <video controls autoplay loop src="https://cdn-uploads.huggingface.co/production/uploads/633c1daf31c06121a58f2df9/b9i-fb2Twh8XmBBNf2DRG.mp4"></video>  |
| plunder     | 9,000,000       | 1,000,000      | 22.19  | <video controls autoplay loop src="https://cdn-uploads.huggingface.co/production/uploads/633c1daf31c06121a58f2df9/JPeGNOVzrotuYUjfzZj40.mp4"></video>  |
| starpilot   | 9,000,000       | 1,000,000      | 49.94  | <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 10M steps (data points).

## Dataset Creation
The dataset was created by training an RL agent with [PPO](https://arxiv.org/abs/1707.06347) for 25M 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.