Developing cooperative policies for multi-stage reinforcement learning tasks
This addresses the challenge of long time horizon tasks in reinforcement learning, though it appears incremental as it builds on existing hierarchical methods.
The paper tackled the problem of hierarchical reinforcement learning algorithms using independent skills by proposing Cooperative Consecutive Policies (CCP) to enable cooperative skills for multi-stage tasks, resulting in outperformance over naive policies, a single agent, and another sequential HRL algorithm in maze and manipulation domains.
Many hierarchical reinforcement learning algorithms utilise a series of independent skills as a basis to solve tasks at a higher level of reasoning. These algorithms don't consider the value of using skills that are cooperative instead of independent. This paper proposes the Cooperative Consecutive Policies (CCP) method of enabling consecutive agents to cooperatively solve long time horizon multi-stage tasks. This method is achieved by modifying the policy of each agent to maximise both the current and next agent's critic. Cooperatively maximising critics allows each agent to take actions that are beneficial for its task as well as subsequent tasks. Using this method in a multi-room maze domain and a peg in hole manipulation domain, the cooperative policies were able to outperform a set of naive policies, a single agent trained across the entire domain, as well as another sequential HRL algorithm.