Noise, overestimation and exploration in Deep Reinforcement Learning

25 Jun 2020 • Rafael Stekolshchik

We will discuss some statistical noise related phenomena, that were investigated by different authors in the framework of Deep Reinforcement Learning algorithms. The following algorithms are touched: DQN, Double DQN, DDPG, TD3, Hill-Climbing... (read more)

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METHOD	TYPE
Q-Learning	Off-Policy TD Control
Target Policy Smoothing	Regularization
Double Q-learning	Off-Policy TD Control
DQN	Q-Learning Networks
Convolution	Convolutions
Weight Decay	Regularization
Adam	Stochastic Optimization
ReLU	Activation Functions
Batch Normalization	Normalization
Double DQN	Q-Learning Networks
Experience Replay	Replay Memory
DDPG	Policy Gradient Methods
Dense Connections	Feedforward Networks
Clipped Double Q-learning	Off-Policy TD Control
TD3	Policy Gradient Methods

METHOD

TYPE

Q-Learning