Learning Synthetic Environments for Reinforcement Learning with Evolution Strategies

24 Jan 2021 Fabio Ferreira Thomas Nierhoff Frank Hutter

This work explores learning agent-agnostic synthetic environments (SEs) for Reinforcement Learning. SEs act as a proxy for target environments and allow agents to be trained more efficiently than when directly trained on the target environment... (read more)

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Methods used in the Paper


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