Entropy Regularization Explained

Entropy Regularization

Introduced by Mnih et al. in Asynchronous Methods for Deep Reinforcement Learning

Entropy Regularization is a type of regularization used in reinforcement learning. For on-policy policy gradient based methods like A3C, the same mutual reinforcement behaviour leads to a highly-peaked $\pi\left(a\mid{s}\right)$ towards a few actions or action sequences, since it is easier for the actor and critic to overoptimise to a small portion of the environment. To reduce this problem, entropy regularization adds an entropy term to the loss to promote action diversity:

$$H(X) = -\sum\pi\left(x\right)\log\left(\pi\left(x\right)\right) $$

Image Credit: Wikipedia

Source: Asynchronous Methods for Deep Reinforcement Learning

Paper Code Snippet

Latest Papers

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Tasks

TASK	PAPERS	SHARE
Continuous Control	19	15.20%
Atari Games	16	12.80%
Image Classification	6	4.80%
Domain Adaptation	6	4.80%
Decision Making	6	4.80%
adversarial training	5	4.00%
Multi-agent Reinforcement Learning	4	3.20%
Object Detection	3	2.40%
Semantic Segmentation	3	2.40%

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