Lenient Multi-Agent Deep Reinforcement Learning

TL;DR

This paper introduces leniency into multi-agent deep reinforcement learning to improve cooperation and convergence in stochastic environments, demonstrating superior performance over existing methods in complex cooperative tasks.

Contribution

It applies leniency to multi-agent DRL, creating a new algorithm called Lenient-DQN that enhances cooperation and convergence in stochastic multi-agent environments.

Findings

LDQN outperforms HDQN and scheduled-HDQN in stochastic CMOTP tasks.

Leniency facilitates cooperation in multi-agent DRL.

LDQN converges more reliably to optimal policies.

Abstract

Much of the success of single agent deep reinforcement learning (DRL) in recent years can be attributed to the use of experience replay memories (ERM), which allow Deep Q-Networks (DQNs) to be trained efficiently through sampling stored state transitions. However, care is required when using ERMs for multi-agent deep reinforcement learning (MA-DRL), as stored transitions can become outdated because agents update their policies in parallel [11]. In this work we apply leniency [23] to MA-DRL. Lenient agents map state-action pairs to decaying temperature values that control the amount of leniency applied towards negative policy updates that are sampled from the ERM. This introduces optimism in the value-function update, and has been shown to facilitate cooperation in tabular fully-cooperative multi-agent reinforcement learning problems. We evaluate our Lenient-DQN (LDQN) empirically against the related Hysteretic-DQN (HDQN) algorithm [22] as well as a modified version we call scheduled-HDQN, that uses average reward learning near terminal states. Evaluations take place in extended variations of the Coordinated Multi-Agent Object Transportation Problem (CMOTP) [8] which include fully-cooperative sub-tasks and stochastic rewards. We find that LDQN agents are more likely to converge to the optimal policy in a stochastic reward CMOTP compared to standard and scheduled-HDQN agents.