pgx 0.9.0

GPU/TPU-accelerated parallel game simulators for reinforcement learning (RL)

A collection of GPU/TPU-accelerated parallel game simulators for reinforcement learning (RL)

Why Pgx?

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

Installation

pip install pgx

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:

pip install pgx-minatar

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.

Usage

Note that all step functions in Pgx environments are JAX-native., i.e., they are all JIT-able.

import jax
import pgx

env = pgx.make("go_19x19")
init = jax.jit(jax.vmap(env.init))  # vectorize and JIT-compile
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 Google Colab:

Supported games

Backgammon	Chess	Shogi	Go

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>)

You can check the current version of each environment by

>>> env.version

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.
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.

• Bridge Bidding and Mahjong environments are under development 🚧
• Five-word descriptions were generated by ChatGPT 🤖

See also

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

Citation

@article{koyamada2023pgx,
  title={Pgx: Hardware-accelerated parallel game simulation 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}
}

LICENSE

Apache-2.0