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--- |
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language: |
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- en |
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license: apache-2.0 |
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size_categories: |
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- 100K<n<1M |
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task_categories: |
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- reinforcement-learning |
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pretty_name: Procgen Benchmark Dataset |
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dataset_info: |
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- config_name: bigfish |
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features: |
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- name: observation |
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dtype: |
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array3_d: |
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shape: |
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- 64 |
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- 64 |
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- 3 |
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dtype: uint8 |
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- name: action |
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dtype: uint8 |
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- name: reward |
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dtype: float32 |
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- name: done |
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dtype: bool |
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- name: truncated |
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dtype: bool |
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splits: |
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- name: train |
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num_bytes: 26043525000 |
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num_examples: 900000 |
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- name: test |
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num_bytes: 2893725000 |
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num_examples: 100000 |
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download_size: 3128341675 |
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dataset_size: 28937250000 |
|
- config_name: bossfight |
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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: 9295623234 |
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dataset_size: 28937250000 |
|
- config_name: miner |
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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 |
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dtype: bool |
|
splits: |
|
- name: train |
|
num_bytes: 26043525000 |
|
num_examples: 900000 |
|
- name: test |
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num_bytes: 2893725000 |
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num_examples: 100000 |
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download_size: 1895918513 |
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dataset_size: 28937250000 |
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configs: |
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- config_name: bigfish |
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data_files: |
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- split: train |
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path: bigfish/train-* |
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- split: test |
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path: bigfish/test-* |
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- config_name: bossfight |
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data_files: |
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- split: train |
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path: bossfight/train-* |
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- split: test |
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path: bossfight/test-* |
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- config_name: miner |
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data_files: |
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- split: train |
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path: miner/train-* |
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- split: test |
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path: miner/test-* |
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tags: |
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- procgen |
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- bigfish |
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- benchmark |
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- openai |
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- bossfight |
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- caveflyer |
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- chaser |
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- climber |
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- dodgeball |
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- fruitbot |
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- heist |
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- jumper |
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- leaper |
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- maze |
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- miner |
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- ninja |
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- plunder |
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- starpilot |
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--- |
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# Procgen Benchmark |
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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. |
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Disclaimer: This is not an official repository from OpenAI. |
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## Dataset Usage |
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Regular usage (for environment bigfish): |
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```python |
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from datasets import load_dataset |
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train_dataset = load_dataset("EpicPinkPenguin/procgen", name="bigfish", split="train") |
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test_dataset = load_dataset("EpicPinkPenguin/procgen", name="bigfish", split="test") |
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``` |
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Usage with PyTorch (for environment bossfight): |
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```python |
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from datasets import load_dataset |
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train_dataset = load_dataset("EpicPinkPenguin/procgen", name="bossfight", split="train").with_format("torch") |
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test_dataset = load_dataset("EpicPinkPenguin/procgen", name="bossfight", split="test").with_format("torch") |
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``` |
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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 | Return | |
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|:------------|:-------| |
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| bigfish | 32.77 | |
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| bossfight | 12.49 | |
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| caveflyer | xx.xx | |
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| chaser | xx.xx | |
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| climber | xx.xx | |
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| coinrun | xx.xx | |
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| dodgeball | xx.xx | |
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| fruitbot | xx.xx | |
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| heist | xx.xx | |
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| jumper | xx.xx | |
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| leaper | xx.xx | |
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| maze | xx.xx | |
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| miner | xx.xx | |
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| ninja | xx.xx | |
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| plunder | xx.xx | |
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| starpilot | xx.xx | |
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## Dataset Structure |
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### Data Instances |
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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}). |
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```json |
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{'action': 1, |
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'done': False, |
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'observation': [[[0, 166, 253], |
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[0, 174, 255], |
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[0, 170, 251], |
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[0, 191, 255], |
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[0, 191, 255], |
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[0, 221, 255], |
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[0, 243, 255], |
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[0, 248, 255], |
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[0, 243, 255], |
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[10, 239, 255], |
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[25, 255, 255], |
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[0, 241, 255], |
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[0, 235, 255], |
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[17, 240, 255], |
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[10, 243, 255], |
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[27, 253, 255], |
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[39, 255, 255], |
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[58, 255, 255], |
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[85, 255, 255], |
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[111, 255, 255], |
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[135, 255, 255], |
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[151, 255, 255], |
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[173, 255, 255], |
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... |
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[0, 0, 37], |
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[0, 0, 39]]], |
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'reward': 0.0, |
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'truncated': False} |
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``` |
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### Data Fields |
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- `observation`: The current RGB observation from the environment. |
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- `action`: The action predicted by the agent for the current observation. |
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- `reward`: The received reward from stepping the environment with the current action. |
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- `done`: If the new observation is the start of a new episode. Obtained after stepping the environment with the current action. |
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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 1M steps (data points). |
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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 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. |
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## Video Samples |
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Here is a collection of videos with the RGB observations from the dataset. |
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| Environment | Observation | |
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|:------------|:------------| |
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| bigfish | <video controls autoplay loop src="https://cdn-uploads.huggingface.co/production/uploads/633c1daf31c06121a58f2df9/lHQXBqLdoWicXlt68I9QX.mp4"></video> | |
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| bossfight | <video controls autoplay loop src="https://cdn-uploads.huggingface.co/production/uploads/633c1daf31c06121a58f2df9/LPoafGi4YBWqqkuFlEN_l.mp4"></video> | |
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| caveflyer | <video controls autoplay loop src="https://cdn-uploads.huggingface.co/production/uploads/633c1daf31c06121a58f2df9/XVqRwu_9yfX4ECQc4At4G.mp4"></video> | |
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| chaser | <video controls autoplay loop src="https://cdn-uploads.huggingface.co/production/uploads/633c1daf31c06121a58f2df9/FIKVv48SThqiC1Z2PYQ7U.mp4"></video> | |
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| climber | <video controls autoplay loop src="https://cdn-uploads.huggingface.co/production/uploads/633c1daf31c06121a58f2df9/XJQlA7IyF9_gwUiw-FkND.mp4"></video> | |
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| coinrun | <video controls autoplay loop src="https://cdn-uploads.huggingface.co/production/uploads/633c1daf31c06121a58f2df9/Ucv3HZttewMRQzTL8r_Tw.mp4"></video> | |
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| dodgeball | <video controls autoplay loop src="https://cdn-uploads.huggingface.co/production/uploads/633c1daf31c06121a58f2df9/5HetbKuXBpO-v1jcVyLTU.mp4"></video> | |
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| fruitbot | <video controls autoplay loop src="https://cdn-uploads.huggingface.co/production/uploads/633c1daf31c06121a58f2df9/zKCyxXvauXjUac-5kEAWz.mp4"></video> | |
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| heist | <video controls autoplay loop src="https://cdn-uploads.huggingface.co/production/uploads/633c1daf31c06121a58f2df9/AdZ6XNmUN5_00BKd9BN8R.mp4"></video> | |
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| jumper | <video controls autoplay loop src="https://cdn-uploads.huggingface.co/production/uploads/633c1daf31c06121a58f2df9/s5k31gWK2Vc6Lp6QVzQXA.mp4"></video> | |
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| leaper | <video controls autoplay loop src="https://cdn-uploads.huggingface.co/production/uploads/633c1daf31c06121a58f2df9/_hDMocxjmzutc0t5FfoTX.mp4"></video> | |
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| maze | <video controls autoplay loop src="https://cdn-uploads.huggingface.co/production/uploads/633c1daf31c06121a58f2df9/uhNdDPuNhZpxVns91Ba-9.mp4"></video> | |
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| miner | <video controls autoplay loop src="https://cdn-uploads.huggingface.co/production/uploads/633c1daf31c06121a58f2df9/ElpJ8l2WHJGrprZ3-giHU.mp4"></video> | |
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| ninja | <video controls autoplay loop src="https://cdn-uploads.huggingface.co/production/uploads/633c1daf31c06121a58f2df9/b9i-fb2Twh8XmBBNf2DRG.mp4"></video> | |
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| plunder | <video controls autoplay loop src="https://cdn-uploads.huggingface.co/production/uploads/633c1daf31c06121a58f2df9/JPeGNOVzrotuYUjfzZj40.mp4"></video> | |
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| starpilot | <video controls autoplay loop src="https://cdn-uploads.huggingface.co/production/uploads/633c1daf31c06121a58f2df9/wY9lZgkw5tor19hCWmm6A.mp4"></video> | |
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## Procgen Benchmark |
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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. |
