Centralized Training with Decentralized Execution

In this final video, the speaker discusses the difference between centralized and decentralized control in multi-agent systems. In centralized control, one agent controls multiple platforms, while in decentralized control, each agent controls its own platform independently. The speaker emphasizes a preference for decentralized control, which allows for more flexibility and autonomy among agents.

The speaker also introduces the concept of training versus execution, emphasizing that what can be done during training may differ significantly from what is done during execution. This difference is crucial in multi-agent systems, where training can include a range of activities such as practicing agility, speed, flexibility, and tactics, which may not be possible during execution.

The video goes on to discuss various algorithms and methods for implementing multi-agent systems, such as Independent Actor Critic, Centralized Critic, and sharing weights. Each method has its advantages and disadvantages, and the choice depends on the specific requirements of the system. The speaker recommends starting with an Actor Critic method with a centralized critic as a starting point for implementing a multi-agent system.

The speaker concludes by highlighting the importance of the policy, which is the ultimate object deployed to the real world. The policy is used by the agents to control their platforms during execution, and it is crucial that the policy is well-trained and capable of handling the real-world environment. The speaker encourages discussion and experimentation to find the best solution for a given multi-agent system.