Mitigating Suboptimality of Deterministic Policy Gradients in Complex Q-functions
Keywords: Deterministic Policy Gradients, Complex Q-functions, Actor-critic RL, TD3.
TL;DR: We identify the challenge of deterministic policy gradients getting stuck in local optima in tasks with complex Q-functions and propose a new actor architecture to find better optima.
Abstract: In reinforcement learning, off-policy actor-critic approaches like DDPG and TD3 are based on the deterministic policy gradient. Herein, the Q-function is trained from off-policy environment data and the actor (policy) is trained to maximize the Q-function via gradient ascent. We observe that in complex tasks like dexterous manipulation and restricted locomotion, the Q-value is a complex function of action, having several local optima or discontinuities. This poses a challenge for gradient ascent to traverse and makes the actor prone to get stuck at local optima. To address this, we introduce a new actor architecture that combines two simple insights: (i) use multiple actors and evaluate the Q-value maximizing action, and (ii) learn surrogates to the Q-function that are simpler to optimize with gradient-based methods.
We evaluate tasks such as restricted locomotion, dexterous manipulation, and large discrete-action space recommender systems and show that our actor finds more optimal actions and outperforms alternate actor architectures.
Supplementary Material: zip
Primary Area: reinforcement learning
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Submission Number: 4137
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