metadata
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:
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dtype:
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- config_name: caveflyer
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- config_name: climber
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- config_name: coinrun
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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: chaser
data_files:
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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:
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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:
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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:
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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 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 | Steps (Train) | Steps (Test) | Return | Observation |
|---|---|---|---|---|
| bigfish | 9,000,000 | 1,000,000 | 29.38 | |
| bossfight | 9,000,000 | 1,000,000 | 10.86 | |
| caveflyer | 9,000,000 | 1,000,000 | 09.58 | |
| chaser | 9,000,000 | 1,000,000 | 11.52 | |
| climber | 9,000,000 | 1,000,000 | 10.66 | |
| coinrun | 9,000,000 | 1,000,000 | 09.84 | |
| dodgeball | 9,000,000 | 1,000,000 | 16.73 | |
| fruitbot | 9,000,000 | 1,000,000 | 21.39 | |
| heist | 9,000,000 | 1,000,000 | 09.95 | |
| jumper | 9,000,000 | 1,000,000 | 08.76 | |
| leaper | 9,000,000 | 1,000,000 | 07.50 | |
| maze | 9,000,000 | 1,000,000 | 09.99 | |
| miner | 9,000,000 | 1,000,000 | 12.66 | |
| ninja | 9,000,000 | 1,000,000 | 09.61 | |
| plunder | 9,000,000 | 1,000,000 | 25.68 | |
| starpilot | 9,000,000 | 1,000,000 | 57.25 |
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 10M 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 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, 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.