Tianshou: Basic API Usage¶
This tutorial is a simple example of how to use Tianshou with a PettingZoo environment.
It demonstrates a game betwenen two random policy agents in the rock-paper-scissors environment.
Environment Setup¶
To follow this tutorial, you will need to install the dependencies shown below. It is recommended to use a newly-created virtual environment to avoid dependency conflicts.
numpy<2.0.0
pettingzoo[classic]>=1.23.0
packaging>=21.3
tianshou==0.5.0
Code¶
The following code should run without any issues. The comments are designed to help you understand how to use PettingZoo with Tianshou. If you have any questions, please feel free to ask in the Discord server.
"""This is a minimal example to show how to use Tianshou with a PettingZoo environment. No training of agents is done here.
Author: Will (https://github.com/WillDudley)
Python version used: 3.8.10
Requirements:
pettingzoo == 1.22.0
git+https://github.com/thu-ml/tianshou
"""
from tianshou.data import Collector
from tianshou.env import DummyVectorEnv, PettingZooEnv
from tianshou.policy import MultiAgentPolicyManager, RandomPolicy
from pettingzoo.classic import rps_v2
if __name__ == "__main__":
# Step 1: Load the PettingZoo environment
env = rps_v2.env(render_mode="human")
# Step 2: Wrap the environment for Tianshou interfacing
env = PettingZooEnv(env)
# Step 3: Define policies for each agent
policies = MultiAgentPolicyManager([RandomPolicy(), RandomPolicy()], env)
# Step 4: Convert the env to vector format
env = DummyVectorEnv([lambda: env])
# Step 5: Construct the Collector, which interfaces the policies with the vectorised environment
collector = Collector(policies, env)
# Step 6: Execute the environment with the agents playing for 1 episode, and render a frame every 0.1 seconds
result = collector.collect(n_episode=1, render=0.1)