Mitigating Suboptimality of Deterministic Policy Gradients in Complex Q-functions

TL;DR

This paper introduces SAVO, an actor architecture for reinforcement learning that generates multiple actions and refines Q-function approximation to overcome local optima issues in complex tasks.

Contribution

SAVO is a novel actor architecture that improves deterministic policy gradients by handling local optima through multiple proposals and Q-function truncation.

Findings

SAVO outperforms existing architectures in complex tasks.

It finds optimal actions more frequently.

Demonstrates effectiveness in diverse environments.

Abstract

In reinforcement learning, off-policy actor-critic methods like DDPG and TD3 use deterministic policy gradients: the Q-function is learned from environment data, while the actor maximizes it via gradient ascent. We observe that in complex tasks such as dexterous manipulation and restricted locomotion with mobility constraints, the Q-function exhibits many local optima, making gradient ascent prone to getting stuck. To address this, we introduce SAVO, an actor architecture that (i) generates multiple action proposals and selects the one with the highest Q-value, and (ii) approximates the Q-function repeatedly by truncating poor local optima to guide gradient ascent more effectively. We evaluate tasks such as restricted locomotion, dexterous manipulation, and large discrete-action space recommender systems and show that our actor finds optimal actions more frequently and outperforms alternate actor architectures.