Addressing Function Approximation Error in Actor-Critic Methods

In value-based reinforcement learning methods such as deep Q-learning, function approximation errors are known to lead to overestimated value estimates and suboptimal policies. We show that this problem persists in an actor-critic setting and propose novel mechanisms to minimize its effects on both the actor and the critic... (read more)

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Methods used in the Paper


METHOD TYPE
Double Q-learning
Off-Policy TD Control
Experience Replay
Replay Memory
Dense Connections
Feedforward Networks
ReLU
Activation Functions
Adam
Stochastic Optimization
Target Policy Smoothing
Regularization
Clipped Double Q-learning
Off-Policy TD Control
TD3
Policy Gradient Methods