Off-Policy Average Reward Actor-Critic with Deterministic Policy Search

• URL: http://arxiv.org/abs/2305.12239v2
• Date: Wed, 19 Jul 2023 05:32:04 GMT
• Title: Off-Policy Average Reward Actor-Critic with Deterministic Policy Search
• Authors: Naman Saxena, Subhojyoti Khastigir, Shishir Kolathaya, Shalabh Bhatnagar
• Abstract summary: We present both on-policy and off-policy deterministic policy gradient theorems for the average reward performance criterion. We also present an Average Reward Off-Policy Deep Deterministic Policy Gradient (ARO-DDPG) algorithm. We compare the average reward performance of our proposed ARO-DDPG and observe better empirical performance compared to state-of-the-art on-policy average reward actor-critic algorithms over MuJoCo-based environments.
• Score: 3.551625533648956
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The average reward criterion is relatively less studied as most existing works in the Reinforcement Learning literature consider the discounted reward criterion. There are few recent works that present on-policy average reward actor-critic algorithms, but average reward off-policy actor-critic is relatively less explored. In this work, we present both on-policy and off-policy deterministic policy gradient theorems for the average reward performance criterion. Using these theorems, we also present an Average Reward Off-Policy Deep Deterministic Policy Gradient (ARO-DDPG) Algorithm. We first show asymptotic convergence analysis using the ODE-based method. Subsequently, we provide a finite time analysis of the resulting stochastic approximation scheme with linear function approximator and obtain an $\epsilon$-optimal stationary policy with a sample complexity of $\Omega(\epsilon^{-2.5})$. We compare the average reward performance of our proposed ARO-DDPG algorithm and observe better empirical performance compared to state-of-the-art on-policy average reward actor-critic algorithms over MuJoCo-based environments.

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