Interpretable End-to-end Urban Autonomous Driving with Latent Deep Reinforcement Learning

Unlike popular modularized framework, end-to-end autonomous driving seeks to solve the perception, decision and control problems in an integrated way, which can be more adapting to new scenarios and easier to generalize at scale. However, existing end-to-end approaches are often lack of interpretability, and can only deal with simple driving tasks like lane keeping... (read more)

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Methods used in the Paper


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