Tianshou: Basic API Usage#
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.
pettingzoo[classic]==1.22.3 packaging==21.3 git+https://github.com/WillDudley/tianshou.git
The following code should run without any issues. The comments are designed to help you understand how to use PettingZoo with CleanRL. 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)