Sample-Efficient Automated Deep Reinforcement Learning

Despite significant progress in challenging problems across various domains, applying state-of-the-art deep reinforcement learning (RL) algorithms remains challenging due to their sensitivity to the choice of hyperparameters. This sensitivity can partly be attributed to the non-stationarity of the RL problem, potentially requiring different hyperparameter settings at various stages of the learning process... (read more)

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Methods used in the Paper


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