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Actor-Critic Reinforcement Learning with Phased Actor
We prove qualitative properties of PAAC for learning convergence of the value and policy, solution optimality, and stability of system dynamics.
Mitigating Estimation Errors by Twin TD-Regularized Actor and Critic for Deep Reinforcement Learning
We address the issue of estimation bias in deep reinforcement learning (DRL) by introducing solution mechanisms that include a new, twin TD-regularized actor-critic (TDR) method.
Long N-step Surrogate Stage Reward to Reduce Variances of Deep Reinforcement Learning in Complex Problems
However, there is a lack of comprehensive and systematic study on this important aspect to demonstrate the effectiveness of multi-step methods in solving highly complex continuous control problems.
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