MinAtar Freeway
Usage
Note that the MinAtar suite is provided as a separate extension for Pgx (pgx-minatar). Therefore, please run the following command additionaly to use the MinAtar suite in Pgx:
Then, you can use the environment as follows:
Description
MinAtar is originally proposed by [Young&Tian+19].
The Pgx implementation is intended to be the exact copy of the original MinAtar implementation in JAX. The Freeway environment is described as follows:
The player begins at the bottom of the screen and motion is restricted to traveling up and down. Player speed is also restricted such that the player can only move every 3 frames. A reward of +1 is given when the player reaches the top of the screen, at which point the player is returned to the bottom. Cars travel horizontally on the screen and teleport to the other side when the edge is reached. When hit by a car, the player is returned to the bottom of the screen. Car direction and speed is indicated by 5 trail channels, the location of the trail gives direction while the specific channel indicates how frequently the car moves (from once every frame to once every 5 frames). Each time the player successfully reaches the top of the screen, the car speeds are randomized. Termination occurs after 2500 frames have elapsed.
Specs
| Name | Value |
|---|---|
| Version | v0 |
| Number of players | 1 |
| Number of actions | 3 |
| Observation shape | (10, 10, 7) |
| Observation type | bool |
| Rewards | {0, 1} |
Observation
| Index | Channel |
|---|---|
[:, :, 0] |
Chicken |
[:, :, 1] |
Car |
[:, :, 2] |
Speed 1 |
[:, :, 3] |
Speed 2 |
[:, :, 4] |
Speed 3 |
[:, :, 5] |
Speed 4 |
Action
No-op (0), up (1), or down (2).
Version History
v1: Specify rng key explicitly (API v2) by @sotetsuk in #1058 (v2.0.0)v0: Initial release (v1.0.0)
Training example
For MinAtar environments, we provide a PPO training example, which takes only 1 min to train on a single GPU.
Baseline models
We provide a baseline model for the MinAtar Freeway environment, which reasonably plays the game.
We trained the model with PPO for 20M steps. See wandb report for the details of the training.
Reference
[Young&Tian+19]"Minatar: An atari-inspired testbed for thorough and reproducible reinforcement learning experiments" arXiv:1903.03176
LICENSE
Pgx is provided under the Apache 2.0 License, but the original MinAtar suite follows the GPL 3.0 License. Therefore, please note that the separated MinAtar extension for Pgx also adheres to the GPL 3.0 License.