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Conference Publication

Sample-Efficient Model-Free Reinforcement Learning with Off-Policy Critics

Host Publication: European Conference on Machine Learning 2019

Authors: D. Steckelmacher, H. Plisnier, D. Roijers and A. Nowé

Publisher: Springer

Publication Year: 2020

Number of Pages: 16

ISBN: 978-3-030-46132-4


Abstract:

Value-based reinforcement-learning algorithms provide state-of-the-art results in model-free discrete-action settings, and tend to outperform actor-critic algorithms. We argue that actor-critic algorithms are limited by their need for an on-policy critic. We propose Bootstrapped Dual Policy Iteration (BDPI), a novel model-free reinforcement-learning algorithm for continuous states and discrete actions, with an actor and several off-policy critics. Off-policy critics are compatible with experience replay, ensuring high sample-efficiency, without the need for off-policy corrections. The actor, by slowly imitating the average greedy policy of the critics, leads to high-quality and state-specific exploration, which we compare to Thompson sampling. Because the actor and critics are fully decoupled, BDPI is remarkably stable, and unusually robust to its hyper-parameters. BDPI is significantly more sample-efficient than Bootstrapped DQN, PPO, and ACKTR, on discrete, continuous and pixel-based tasks.

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