Deep Coherent Exploration For Continuous Control

1 Jan 2021 Anonymous

In policy search methods for reinforcement learning (RL), exploration is often performed by injecting noise either in action space at each step independently or in parameter space over each full trajectory. In prior work, it has been shown that with linear policies, a more balanced trade-off between these two exploration strategies is beneficial... (read more)

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Methods used in the Paper


METHOD TYPE
Dilated Convolution
Convolutions
Global Average Pooling
Pooling Operations
Average Pooling
Pooling Operations
Convolution
Convolutions
1x1 Convolution
Convolutions
SAC
Convolutions
Entropy Regularization
Regularization
PPO
Policy Gradient Methods
A2C
Policy Gradient Methods