Proximal Policy Optimization Smoothed Algorithm

4 Dec 2020 Wangshu Zhu Andre Rosendo

Proximal policy optimization (PPO) has yielded state-of-the-art results in policy search, a subfield of reinforcement learning, with one of its key points being the use of a surrogate objective function to restrict the step size at each policy update. Although such restriction is helpful, the algorithm still suffers from performance instability and optimization inefficiency from the sudden flattening of the curve... (read more)

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Methods used in the Paper


METHOD TYPE
Entropy Regularization
Regularization
PPO
Policy Gradient Methods