4 accepted papers
Avoiding catastrophic forgetting for previous tasks and maintaining model plasticity to support new tasks are two critical objectives of continual learning. However, existing methods usually neglect one of the two aspects and fail to support long task sequences with satisfactory performance, especia
While decentralized training is attractive in multi-agent reinforcement learning (MARL) for its excellent scalability and robustness, its inherent coordination challenges in collaborative tasks result in numerous interactions for agents to learn good policies. To alleviate this problem, action advis…
Learning effective strategies in sparse reward tasks is one of the fundamental challenges in reinforcement learning. This becomes extremely difficult in multi-agent environments, as the concurrent learning of multiple agents induces the non-stationarity problem and a sharply increased joint state sp…
Although multistage tasks involving multiple sequential goals are common in real-world applications, they are not fully studied in multi-agent reinforcement learning (MARL). To accomplish a multi-stage task, agents have to achieve cooperation on different subtasks. Exploring the collaborative patter…