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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Methods used in the Paper


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