The proposed algorithms can be extended to settings with more complex communication patterns by incorporating mechanisms for agents to exchange information with each other or for the learner to receive partial feedback.
For agents exchanging information, the algorithms can be modified to allow agents to share their experiences or decisions with each other. This can help in collaborative learning scenarios where agents can benefit from each other's knowledge and insights. By incorporating communication channels between agents, the algorithms can adapt to a more interconnected network of learning entities.
In the case of partial feedback, the algorithms can be adjusted to handle situations where only partial information about the actions or rewards is received. This can be useful in scenarios where the learner may not have complete visibility into the outcomes of the actions taken by the agents. By incorporating mechanisms to handle partial feedback, the algorithms can improve decision-making processes in environments with limited information.
Overall, extending the algorithms to settings with more complex communication patterns involves enhancing the communication protocols and feedback mechanisms to accommodate the specific requirements of the multi-agent learning environment.