Deep Learning to Learn
The problem with interacting, non-communicating agents is much more difficult than avoiding stationary objects. Each agent has only a portion of the information needed to compute the optimal or even a feasible action. Another difficulty is that, unless other agents are treated as non-interacting, the computational burden could quickly explode. The talk reviews some of the approaches including CBFs and Reinforcement Learning. A CBF-based approach developed in-house showed a very good performance avoiding collisions not only when all the agents are implementing the same algorithm, but also when an agent becomes non-interacting or even actively pursues another agent. The computational load remains reasonable with each agent, as the host, being able to handle up to 20 to 25 interacting targets.