Policy Gradient using Weak Derivatives for Reinforcement Learning

• URL: http://arxiv.org/abs/2004.04843v1
• Date: Thu, 9 Apr 2020 23:05:18 GMT
• Title: Policy Gradient using Weak Derivatives for Reinforcement Learning
• Authors: Sujay Bhatt, Alec Koppel, Vikram Krishnamurthy
• Abstract summary: This paper considers policy search in continuous state-action reinforcement learning problems. The gradient estimates obtained using weak derivatives is shown to be lower than those obtained using the popular score-function approach.
• Score: 24.50189361694407
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper considers policy search in continuous state-action reinforcement learning problems. Typically, one computes search directions using a classic expression for the policy gradient called the Policy Gradient Theorem, which decomposes the gradient of the value function into two factors: the score function and the Q-function. This paper presents four results:(i) an alternative policy gradient theorem using weak (measure-valued) derivatives instead of score-function is established; (ii) the stochastic gradient estimates thus derived are shown to be unbiased and to yield algorithms that converge almost surely to stationary points of the non-convex value function of the reinforcement learning problem; (iii) the sample complexity of the algorithm is derived and is shown to be $O(1/\sqrt(k))$; (iv) finally, the expected variance of the gradient estimates obtained using weak derivatives is shown to be lower than those obtained using the popular score-function approach. Experiments on OpenAI gym pendulum environment show superior performance of the proposed algorithm.

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