A collection of GPU/TPU-accelerated parallel game simulators for reinforcement learning (RL)
🎉 v1.0.0 is released! (2023.6.20)
Brax, a JAX-native physics engine, provides extremely high-speed parallel simulation for RL in continuous state space. Then, what about RL in discrete state spaces like Chess, Shogi, and Go? Pgx provides a wide variety of JAX-native game simulators! Highlighted features include:
- ⚡ Super fast in parallel execution on accelerators
- 🎲 Various game support including Backgammon, Chess, Shogi, and Go
- 🖼️ Beautiful visualization in SVG format
The following code snippet shows a simple example of using Pgx.
You can try it out in this Colab.
Note that all step functions in Pgx environments are JAX-native., i.e., they are all JIT-able.
Please refer to the documentation for more details.
import jax
import pgx
env = pgx.make("go_19x19")
init = jax.jit(jax.vmap(env.init))
step = jax.jit(jax.vmap(env.step))
batch_size = 1024
keys = jax.random.split(jax.random.PRNGKey(42), batch_size)
state = init(keys) # vectorized states
while not (state.terminated | state.truncated).all():
action = model(state.current_player, state.observation, state.legal_action_mask)
state = step(state, action) # state.reward (2,)Pgx is a library that focuses on faster implementations rather than just the API itself. However, the API itself is also sufficiently general. For example, all environments in Pgx can be converted to the AEC API of PettingZoo, and you can run Pgx environments through the PettingZoo API. You can see the demonstration in this Colab.
pip install pgxNote 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:
pip install pgx-minatarPgx 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.
| Backgammon | Chess | Shogi | Go |
|---|---|---|---|
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  |
  |
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Use pgx.available_envs() -> Tuple[EnvId] to see the list of currently available games. Given an <EnvId>, you can create the environment via
>>> env = pgx.make(<EnvId>)| Game/EnvId | Visualization | Version | Five-word description |
|---|---|---|---|
2048 "2048" |
  |
v0 |
Merge tiles to create 2048. |
Animal Shogi"animal_shogi" |
  |
v0 |
Animal-themed child-friendly shogi. |
Backgammon"backgammon" |
  |
v0 |
Luck aids bearing off checkers. |
Bridge bidding"bridge_bidding" |
  |
v0 |
Partners exchange information via bids. |
Chess"chess" |
  |
v0 |
Checkmate opponent's king to win. |
Connect Four"connect_four" |
  |
v0 |
Connect discs, win with four. |
Gardner Chess"gardner_chess" |
  |
v0 |
5x5 chess variant, excluding castling. |
Go"go_9x9" "go_19x19" |
  |
v0 |
Strategically place stones, claim territory. |
Hex"hex" |
  |
v0 |
Connect opposite sides, block opponent. |
Kuhn Poker"kuhn_poker" |
  |
v0 |
Three-card betting and bluffing game. |
Leduc hold'em"leduc_holdem" |
  |
v0 |
Two-suit, limited deck poker. |
MinAtar/Asterix"minatar-asterix" |
 |
v0 |
Avoid enemies, collect treasure, survive. |
MinAtar/Breakout"minatar-breakout" |
 |
v0 |
Paddle, ball, bricks, bounce, clear. |
MinAtar/Freeway"minatar-freeway" |
 |
v0 |
Dodging cars, climbing up freeway. |
MinAtar/Seaquest"minatar-seaquest" |
 |
v0 |
Underwater submarine rescue and combat. |
MinAtar/SpaceInvaders"minatar-space_invaders" |
 |
v0 |
Alien shooter game, dodge bullets. |
Othello"othello" |
  |
v0 |
Flip and conquer opponent's pieces. |
Shogi"shogi" |
  |
v0 |
Japanese chess with captured pieces. |
Sparrow Mahjong"sparrow_mahjong" |
  |
v0 |
A simplified, children-friendly Mahjong. |
Tic-tac-toe"tic_tac_toe" |
  |
v0 |
Three in a row wins. |
- Mahjong environments are under development 🚧 If you have any requests for new environments, please let us know by opening an issue
- Five-word descriptions were generated by ChatGPT 🤖
Each environment is versioned, and the version is incremented when there are changes that affect the performance of agents or when there are changes that are not backward compatible with the API. If you want to pursue complete reproducibility, we recommend that you check the version of Pgx and each environment as follows:
>>> pgx.__version__
'1.0.0'
>>> env.version
'v0'Pgx is intended to complement these JAX-native environments with (classic) board game suits:
- RobertTLange/gymnax: JAX implementation of popular RL environments (classic control, bsuite, MinAtar, etc) and meta RL tasks
- google/brax: Rigidbody physics simulation in JAX and continuous-space RL tasks (ant, fetch, humanoid, etc)
- instadeepai/jumanji: A suite of diverse and challenging RL environments in JAX (bin-packing, routing problems, etc)
Combining Pgx with these JAX-native algorithms/implementations might be an interesting direction:
- Anakin framework: Highly efficient RL framework that works with JAX-native environments on TPUs
- deepmind/mctx: JAX-native MCTS implementations, including AlphaZero and MuZero
- deepmind/rlax: JAX-native RL components
- google/evojax: Hardware-Accelerated neuroevolution
- RobertTLange/evosax: JAX-native evolution strategy (ES) implementations
- adaptive-intelligent-robotics/QDax: JAX-native Quality-Diversity (QD) algorithms
- luchris429/purejaxrl: Jax-native RL implementations
If you use Pgx in your work, please cite the following paper:
@article{koyamada2023pgx,
title={Pgx: Hardware-accelerated Parallel Game Simulators for Reinforcement Learning},
author={Koyamada, Sotetsu and Okano, Shinri and Nishimori, Soichiro and Murata, Yu and Habara, Keigo and Kita, Haruka and Ishii, Shin},
journal={arXiv preprint arXiv:2303.17503},
year={2023}
}
Apache-2.0