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Expected Policy Gradients for Reinforcement Learning
[article]
2020
arXiv
pre-print
We propose expected policy gradients (EPG), which unify stochastic policy gradients (SPG) and deterministic policy gradients (DPG) for reinforcement learning. Inspired by expected sarsa, EPG integrates (or sums) across actions when estimating the gradient, instead of relying only on the action in the sampled trajectory. For continuous action spaces, we first derive a practical result for Gaussian policies and quadratic critics and then extend it to a universal analytical method, covering a
arXiv:1801.03326v2
fatcat:667mciiqpzgolbscpnflupy5wm