You can train AgentB in a multi-agent reinforcement learning environment while keeping AgentA's policy unchanged. This approach allows AgentB to learn how to effectively cooperate with the pretrained AgentA without affecting AgentA's performance. Here are concise steps and considerations for implementing this strategy:
Fixed Policy for AgentA:
Keep AgentA's policy unchanged during AgentB's training sessions.
Training AgentB:
Update only AgentB's policy based on its received observations and rewards, aiming to improve cooperation with AgentA.
Environment Setup:
Ensure the environment supports interactions between both agents and provides appropriate observations and rewards to each.
Key Considerations:
- Observability: AgentB needs to observe relevant aspects of the environment and AgentA's actions or state.
- Reward Structure: Design AgentB's rewards to encourage effective cooperation with AgentA.
- Exploration: Employ exploration strategies for AgentB to discover cooperative strategies.
Evaluation:
- Regularly assess both AgentB's individual performance and the overall system performance to ensure effective cooperation.
By focusing on these aspects, you can train AgentB to cooperate with a fixed-policy AgentA, enhancing the multi-agent system's performance without compromising the pretrained agent's behavior.
Hope it helps!
