Guaranteed Trust Region Optimization via Two-Phase KL Penalization

• URL: http://arxiv.org/abs/2312.05405v1
• Date: Fri, 8 Dec 2023 23:29:57 GMT
• Title: Guaranteed Trust Region Optimization via Two-Phase KL Penalization
• Authors: K.R. Zentner, Ujjwal Puri, Zhehui Huang, Gaurav S. Sukhatme
• Abstract summary: We show that applying KL penalization alone is nearly sufficient to enforce trust regions. We then show that introducing a "fixup" phase is sufficient to guarantee a trust region is enforced on every policy update. The resulting algorithm, which we call FixPO, is able to train a variety of policy architectures and action spaces.
• Score: 11.008537121214104
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: On-policy reinforcement learning (RL) has become a popular framework for solving sequential decision problems due to its computational efficiency and theoretical simplicity. Some on-policy methods guarantee every policy update is constrained to a trust region relative to the prior policy to ensure training stability. These methods often require computationally intensive non-linear optimization or require a particular form of action distribution. In this work, we show that applying KL penalization alone is nearly sufficient to enforce such trust regions. Then, we show that introducing a "fixup" phase is sufficient to guarantee a trust region is enforced on every policy update while adding fewer than 5% additional gradient steps in practice. The resulting algorithm, which we call FixPO, is able to train a variety of policy architectures and action spaces, is easy to implement, and produces results competitive with other trust region methods.

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