What are the potential limitations or drawbacks of the non-zero-sum game approach used in the MBC system, and how could they be addressed?
The non-zero-sum game approach utilized in the MBC system presents several potential limitations. One significant drawback is the risk of local optima convergence, where the Blind and Perceptive Policies may become overly competitive, leading to suboptimal performance. This competition can hinder the collaborative control necessary for effective locomotion, especially in complex environments.
To address this issue, implementing a more structured collaboration framework could be beneficial. For instance, introducing a shared reward mechanism that incentivizes cooperation rather than competition can encourage both policies to work together more effectively. This could involve designing reward functions that promote successful navigation outcomes when both policies contribute positively, rather than penalizing one for the other's success.
Additionally, enhancing the communication protocols between the policies can facilitate better coordination. By allowing the Blind and Perceptive Policies to share their state information and decision-making processes more transparently, the system can reduce misunderstandings and improve overall performance.
Lastly, incorporating regularization techniques that balance the contributions of each policy can help mitigate the risks associated with competition. By ensuring that both policies are equally represented in decision-making, the MBC system can maintain a more stable and effective collaborative control strategy.