Simple Policy Optimization
Keywords: Reinforcement Learning, Policy Optimization
Abstract: Model-free reinforcement learning algorithms have seen remarkable progress, but key challenges remain. Trust Region Policy Optimization (TRPO) is known for ensuring monotonic policy improvement through conservative updates within a trust region, backed by strong theoretical guarantees. However, its reliance on complex second-order optimization limits its practical efficiency. Proximal Policy Optimization (PPO) addresses this by simplifying TRPO's approach using ratio clipping, improving efficiency but sacrificing some theoretical robustness. This raises a natural question: Can we combine the strengths of both methods? In this paper, we introduce Simple Policy Optimization (SPO), a novel unconstrained first-order algorithm. SPO integrates the surrogate objective with Total Variation (TV) divergence instead of Kullback-Leibler (KL) divergence, achieving a balance between the theoretical rigor of TRPO and the efficiency of PPO. Our new objective improves upon ratio clipping, offering stronger theoretical properties and better constraining the probability ratio within the trust region. Empirical results demonstrate that SPO outperforms PPO with a simple implementation, particularly for training large, complex network architectures end-to-end.
Supplementary Material: zip
Primary Area: reinforcement learning
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Submission Number: 3422
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