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Gymnasium wrapper for PySTK2

Project description

PySuperTuxKart gymnasium wrapper

warning: pystk2-gymnasium is in alpha stage - the environments might change abruptly!

Install

The PySuperKart2 gymnasium wrapper is a Python package, so installing is fairly easy

pip install pystk2-gymnasium

Note that during the first run, SuperTuxKart assets are downloaded in the cache directory.

Environments

Warning only one SuperTuxKart environment can be created for now. Moreover, no graphics information is available for now.

After importing pystk2_gymnasium, the following environments are available:

  • supertuxkart-v0 is the main environment containing complete observations, with the following options:
    • render_mode can be None or human
    • track defines the SuperTuxKart track to use (None for random). The full list can be found in STKRaceEnv.TRACKS after initialization with initialize.initialize(with_graphics: bool) has been called.
    • num_kart defines the number of karts on the track (3 by default)
    • rank_start defines the starting position (None for random, which is the default)
    • use_ai flag (False by default) to ignore actions and use a SuperTuxKart bot
    • max_paths the maximum number of the (nearest) paths (a track is made of paths) to consider in the observation state
    • laps is the number of laps (1 by default)
    • difficulty is the difficulty of the other bots (0 to 2, default to 2)
  • supertuxkart-simple-v0 is a simplified environment with fixed number of observations for paths (controlled by state_paths, default 5), items (state_items, default 5), karts (state_karts, default 5)
  • supertuxkart-flattened-v0 has observation and action spaces simplified at the maximum (only discrete and continuous keys)
  • supertuxkart-flattened-continuous-actions-v0 removes discrete actions (default to 0) so this is steer/acceleration only in the continuous domain
  • supertuxkart-flattened-multidiscrete-v0 is like the previous one, but with fully multi-discrete actions. acceleration_steps and steer_steps (default to 5) control the number of discrete values for acceleration and steering respectively.
  • supertuxkart-flattened-discrete-v0 is like the previous one, but with fully discretized actions

The reward is the distance traveled.

Example

import gymnasium as gym
import pystk2_gymnasium

# Use a a flattened version of the observation and action spaces
# In both case, this corresponds to a dictionary with two keys:
# - `continuous` is a vector corresponding to the continuous observations
# - `discrete` is a vector (of integers) corresponding to discrete observations
env = gym.make("supertuxkart-flattened-v0", render_mode="human", use_ai=False)

ix = 0
done = False
state, *_ = env.reset()

while not done:
    ix += 1
    action = env.action_space.sample()
    state, reward, terminated, truncated, _ = env.step(action)
    done = truncated or terminated

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