Collaborative Evolutionary Reinforcement Learning

Deep reinforcement learning algorithms have been successfully applied to a range of challenging control tasks. However, these methods typically struggle with achieving effective exploration and are extremely sensitive to the choice of hyperparameters... (read more)

PDF Abstract

Results from the Paper


  Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.

Methods used in the Paper


METHOD TYPE
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