Developing cooperative policies for multi-stage reinforcement learning tasks

TL;DR

This paper introduces the Cooperative Consecutive Policies (CCP) method, enabling agents in hierarchical reinforcement learning to cooperate across multiple stages, improving performance on complex tasks.

Contribution

It proposes a novel CCP method that modifies agent policies to maximize both current and next critic, fostering cooperation in multi-stage RL tasks.

Findings

CCP outperforms naive policies in maze and manipulation domains.

Cooperative policies achieve better long-term performance than independent skills.

Method surpasses single-agent and existing sequential HRL algorithms.

Abstract

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.