Robust Deep Reinforcement Learning against Adversarial Perturbations on State Observations

NeurIPS 2020 • Huan Zhang • Hongge Chen • Chaowei Xiao • Bo Li • Mingyan Liu • Duane Boning • Cho-Jui Hsieh

A deep reinforcement learning (DRL) agent observes its states through observations, which may contain natural measurement errors or adversarial noises. Since the observations deviate from the true states, they can mislead the agent into making suboptimal actions... (read more)

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Results from the Paper

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

METHOD

TYPE

Entropy Regularization