pgx 0.7.1

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

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.terminated).all():
    action = model(state.current_player, state.observation, state.legal_action_mask)
    state = step(state, action)  # state.reward (2,)

⚠️ Pgx is currently in the beta version. Therefore, API is subject to change without notice. We aim to release v1.0.0 in April 2023. Opinions and comments are more than welcome!

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"		beta	Merge tiles to create 2048.
Animal Shogi "animal_shogi"		beta	Animal-themed child-friendly shogi.
Backgammon "backgammon"		beta	Luck aids bearing off checkers.
Chess "chess"		beta	Checkmate opponent's king to win.
Connect Four "connect_four"		beta	Connect discs, win with four.
Go "go_9x9" "go_19x19"		beta	Strategically place stones, claim territory.
Hex "hex"		beta	Connect opposite sides, block opponent.
Kuhn Poker "kuhn_poker"		beta	Three-card betting and bluffing game.
Leduc hold'em "leduc_holdem"		beta	Two-suit, limited deck poker.
MinAtar/Asterix "minatar-asterix"		beta	Avoid enemies, collect treasure, survive.
MinAtar/Breakout "minatar-breakout"		beta	Paddle, ball, bricks, bounce, clear.
MinAtar/Freeway "minatar-freeway"		beta	Dodging cars, climbing up freeway.
MinAtar/Seaquest "minatar-seaquest"		beta	Underwater submarine rescue and combat.
MinAtar/SpaceInvaders "minatar-space_invaders"		beta	Alien shooter game, dodge bullets.
Othello "othello"		beta	Flip and conquer opponent's pieces.
Shogi "shogi"		beta	Japanese chess with captured pieces.
Sparrow Mahjong "sparrow_mahjong"		beta	A simplified, children-friendly Mahjong.
Tic-tac-toe "tic_tac_toe"		beta	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