Lenient Multi-Agent Deep Reinforcement Learning


Palmer, Gregory, Tuyls, Karl, Bloembergen, Daan and Savani, Rahul ORCID: 0000-0003-1262-7831 (2018) Lenient Multi-Agent Deep Reinforcement Learning. .

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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care is required when using ERMs for multi-agent deep reinforcement learning (MA-DRL), as stored transitions can become outdated because agents update their policies in parallel [11].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.

Item Type: Conference or Workshop Item (Unspecified)
Additional Information: 9 pages, 6 figures, AAMAS2018 Conference Proceedings
Uncontrolled Keywords: Multi-Agent Deep Reinforcement Learning, Leniency
Depositing User: Symplectic Admin
Date Deposited: 16 Oct 2017 10:16
Last Modified: 19 Jan 2023 06:53
Related URLs:
URI: https://livrepository.liverpool.ac.uk/id/eprint/3009868
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