language:
- en
license: apache-2.0
size_categories:
- 100K<n<1M
task_categories:
- reinforcement-learning
pretty_name: Procgen Benchmark Dataset
dataset_info:
- 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
splits:
- name: train
num_bytes: 26043525000
num_examples: 900000
- name: test
num_bytes: 2893725000
num_examples: 100000
download_size: 4787252231
dataset_size: 28937250000
- 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: 26043525000
num_examples: 900000
- name: test
num_bytes: 2893725000
num_examples: 100000
download_size: 3128341675
dataset_size: 28937250000
configs:
- config_name: bossfight
data_files:
- split: train
path: bossfight/train-*
- split: test
path: bossfight/test-*
- config_name: bigfish
data_files:
- split: train
path: bigfish/train-*
- split: test
path: bigfish/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 reinforcement learning agent trained on each of the 16 procedurally-generated gym environments from the 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):
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):
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}).
{'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 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 | Return |
|---|---|
| bigfish | |
| bossfight | |
| caveflyer | |
| chaser | |
| climber | |
| coinrun | |
| dodgeball | |
| fruitbot | |
| heist | |
| jumper | |
| leaper | |
| maze | |
| miner | |
| ninja | |
| plunder | |
| starpilot |
Procgen Benchmark
The 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.