Smoothed functional-based gradient algorithms for off-policy reinforcement learning: A non-asymptotic viewpoint

• URL: http://arxiv.org/abs/2101.02137v6
• Date: Sun, 23 Jun 2024 18:34:27 GMT
• Title: Smoothed functional-based gradient algorithms for off-policy reinforcement learning: A non-asymptotic viewpoint
• Authors: Nithia Vijayan, Prashanth L. A,
• Abstract summary: We propose two policy gradient algorithms for solving the problem of control in an off-policy reinforcement learning context. Both algorithms incorporate a smoothed functional (SF) based gradient estimation scheme.
• Score: 8.087699764574788
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose two policy gradient algorithms for solving the problem of control in an off-policy reinforcement learning (RL) context. Both algorithms incorporate a smoothed functional (SF) based gradient estimation scheme. The first algorithm is a straightforward combination of importance sampling-based off-policy evaluation with SF-based gradient estimation. The second algorithm, inspired by the stochastic variance-reduced gradient (SVRG) algorithm, incorporates variance reduction in the update iteration. For both algorithms, we derive non-asymptotic bounds that establish convergence to an approximate stationary point. From these results, we infer that the first algorithm converges at a rate that is comparable to the well-known REINFORCE algorithm in an off-policy RL context, while the second algorithm exhibits an improved rate of convergence.

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