Skip to main content

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

Project description

ci

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.

Open In Colab

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:

Open In 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"
beta Merge tiles to create 2048.
Animal Shogi
"animal_shogi"
v0 Animal-themed child-friendly shogi.
Backgammon
"backgammon"
beta 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"
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"
v0 Flip and conquer opponent's pieces.
Shogi
"shogi"
v0 Japanese chess with captured pieces.
Sparrow Mahjong
"sparrow_mahjong"
beta A simplified, children-friendly Mahjong.
Tic-tac-toe
"tic_tac_toe"
v0 Three in a row wins.

See also

Pgx is intended to complement these JAX-native environments with (classic) board game suits:

Combining Pgx with these JAX-native algorithms/implementations might be an interesting direction:

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

Project details


Release history Release notifications | RSS feed

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

pgx-0.8.1.tar.gz (245.7 kB view hashes)

Uploaded Source

Built Distribution

pgx-0.8.1-py3-none-any.whl (326.2 kB view hashes)

Uploaded Python 3

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page