---
language:
- en
license: apache-2.0
size_categories:
- 100K<n<1M
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
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  - name: reward
    dtype: float32
  - name: done
    dtype: bool
  - name: truncated
    dtype: bool
  splits:
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    num_bytes: 26043525000
    num_examples: 900000
  - name: test
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    num_examples: 100000
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- config_name: bossfight
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  - name: observation
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        - 64
        - 64
        - 3
        dtype: uint8
  - name: action
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  - name: reward
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  - name: done
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- config_name: caveflyer
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  - name: observation
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      array3_d:
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        - 64
        - 64
        - 3
        dtype: uint8
  - name: action
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  - name: reward
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  - name: done
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  - name: truncated
    dtype: bool
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- config_name: fruitbot
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        - 64
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        - 3
        dtype: uint8
  - name: action
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  - name: reward
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  - name: done
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    dtype: bool
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    num_examples: 900000
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- config_name: heist
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        - 3
        dtype: uint8
  - name: action
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  - name: reward
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  - name: done
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  - name: truncated
    dtype: bool
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- config_name: miner
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  - name: done
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- config_name: ninja
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        dtype: uint8
  - name: action
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  - name: reward
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  - name: done
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    dtype: bool
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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: 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: 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-*
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 | Return |
|:------------|:-------|
| bigfish     | 32.77  |
| bossfight   | 12.49  |
| caveflyer   | xx.xx  |
| chaser      | xx.xx  |
| climber     | xx.xx  |
| coinrun     | xx.xx  |
| dodgeball   | xx.xx  |
| fruitbot    | xx.xx  |
| heist       | xx.xx  |
| jumper      | xx.xx  |
| leaper      | xx.xx  |
| maze        | xx.xx  |
| miner       | xx.xx  |
| ninja       | xx.xx  |
| plunder     | xx.xx  |
| starpilot   | xx.xx  |


## 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 taking the argmax action at each step, corresponding to taking the mode of the action distribution. Consequently the rollout policy is deterministic. The environments were created on `distribution_mode=easy` and with unlimited levels.

## Video Samples
Here is a collection of videos with the RGB observations from the dataset.

| Environment | Observation |
|:------------|:------------|
| bigfish     | <video controls autoplay loop src="https://cdn-uploads.huggingface.co/production/uploads/633c1daf31c06121a58f2df9/lHQXBqLdoWicXlt68I9QX.mp4"></video>  |
| bossfight   | <video controls autoplay loop src="https://cdn-uploads.huggingface.co/production/uploads/633c1daf31c06121a58f2df9/LPoafGi4YBWqqkuFlEN_l.mp4"></video>  |
| caveflyer   | <video controls autoplay loop src="https://cdn-uploads.huggingface.co/production/uploads/633c1daf31c06121a58f2df9/XVqRwu_9yfX4ECQc4At4G.mp4"></video>  |
| chaser      | <video controls autoplay loop src="https://cdn-uploads.huggingface.co/production/uploads/633c1daf31c06121a58f2df9/FIKVv48SThqiC1Z2PYQ7U.mp4"></video>  |
| climber     | <video controls autoplay loop src="https://cdn-uploads.huggingface.co/production/uploads/633c1daf31c06121a58f2df9/XJQlA7IyF9_gwUiw-FkND.mp4"></video>  |
| coinrun     | <video controls autoplay loop src="https://cdn-uploads.huggingface.co/production/uploads/633c1daf31c06121a58f2df9/Ucv3HZttewMRQzTL8r_Tw.mp4"></video>  |
| dodgeball   | <video controls autoplay loop src="https://cdn-uploads.huggingface.co/production/uploads/633c1daf31c06121a58f2df9/5HetbKuXBpO-v1jcVyLTU.mp4"></video>  |
| fruitbot    | <video controls autoplay loop src="https://cdn-uploads.huggingface.co/production/uploads/633c1daf31c06121a58f2df9/zKCyxXvauXjUac-5kEAWz.mp4"></video>  |
| heist       | <video controls autoplay loop src="https://cdn-uploads.huggingface.co/production/uploads/633c1daf31c06121a58f2df9/AdZ6XNmUN5_00BKd9BN8R.mp4"></video>  |
| jumper      | <video controls autoplay loop src="https://cdn-uploads.huggingface.co/production/uploads/633c1daf31c06121a58f2df9/s5k31gWK2Vc6Lp6QVzQXA.mp4"></video>  |
| leaper      | <video controls autoplay loop src="https://cdn-uploads.huggingface.co/production/uploads/633c1daf31c06121a58f2df9/_hDMocxjmzutc0t5FfoTX.mp4"></video>  |
| maze        | <video controls autoplay loop src="https://cdn-uploads.huggingface.co/production/uploads/633c1daf31c06121a58f2df9/uhNdDPuNhZpxVns91Ba-9.mp4"></video>  |
| miner       | <video controls autoplay loop src="https://cdn-uploads.huggingface.co/production/uploads/633c1daf31c06121a58f2df9/ElpJ8l2WHJGrprZ3-giHU.mp4"></video>  |
| ninja       | <video controls autoplay loop src="https://cdn-uploads.huggingface.co/production/uploads/633c1daf31c06121a58f2df9/b9i-fb2Twh8XmBBNf2DRG.mp4"></video>  |
| plunder     | <video controls autoplay loop src="https://cdn-uploads.huggingface.co/production/uploads/633c1daf31c06121a58f2df9/JPeGNOVzrotuYUjfzZj40.mp4"></video>  |
| starpilot   | <video controls autoplay loop src="https://cdn-uploads.huggingface.co/production/uploads/633c1daf31c06121a58f2df9/wY9lZgkw5tor19hCWmm6A.mp4"></video>  |

## 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.