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

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Teaching a Machine to Read Maps with Deep Reinforcement Learning Gino Brunner • Oliver Richter • Yuyi Wang • Roger Wattenhofer	2017-11-20
AMBER: Adaptive Multi-Batch Experience Replay for Continuous Action Control Seungyul Han • Youngchul Sung	2017-10-12
Sparse Markov Decision Processes with Causal Sparse Tsallis Entropy Regularization for Reinforcement Learning Kyungjae Lee • Sungjoon Choi • Songhwai Oh	2017-09-19
Improving Search through A3C Reinforcement Learning based Conversational Agent Milan Aggarwal • Aarushi Arora • Shagun Sodhani • Balaji Krishnamurthy	2017-09-17
Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation \| Yuhuai Wu • Elman Mansimov • Shun Liao • Roger Grosse • Jimmy Ba	2017-08-17
DARLA: Improving Zero-Shot Transfer in Reinforcement Learning \| Irina Higgins • Arka Pal • Andrei A. Rusu • Loic Matthey • Christopher P Burgess • Alexander Pritzel • Matthew Botvinick • Charles Blundell • Alexander Lerchner	2017-07-26
Learning Transferable Architectures for Scalable Image Recognition \| Barret Zoph • Vijay Vasudevan • Jonathon Shlens • Quoc V. Le # 35 IMAGE CLASSIFICATION ON IMAGENET	2017-07-21
Proximal Policy Optimization Algorithms \| John Schulman • Filip Wolski • Prafulla Dhariwal • Alec Radford • Oleg Klimov	2017-07-20
Noisy Networks for Exploration \| Meire Fortunato • Mohammad Gheshlaghi Azar • Bilal Piot • Jacob Menick • Ian Osband • Alex Graves • Vlad Mnih • Remi Munos • Demis Hassabis • Olivier Pietquin • Charles Blundell • Shane Legg # 1 ATARI GAMES ON ATARI 2600 BOXING	2017-06-30
Learning to Factor Policies and Action-Value Functions: Factored Action Space Representations for Deep Reinforcement learning Sahil Sharma • Aravind Suresh • Rahul Ramesh • Balaraman Ravindran	2017-05-20
Feature Control as Intrinsic Motivation for Hierarchical Reinforcement Learning Nat Dilokthanakul • Christos Kaplanis • Nick Pawlowski • Murray Shanahan	2017-05-18
Equivalence Between Policy Gradients and Soft Q-Learning John Schulman • Xi Chen • Pieter Abbeel	2017-04-21
The Reactor: A fast and sample-efficient Actor-Critic agent for Reinforcement Learning Audrunas Gruslys • Will Dabney • Mohammad Gheshlaghi Azar • Bilal Piot • Marc Bellemare • Remi Munos # 4 ATARI GAMES ON ATARI 2600 CRAZY CLIMBER	2017-04-15
Tactics of Adversarial Attack on Deep Reinforcement Learning Agents Yen-Chen Lin • Zhang-Wei Hong • Yuan-Hong Liao • Meng-Li Shih • Ming-Yu Liu • Min Sun	2017-03-08
Improving Policy Gradient by Exploring Under-appreciated Rewards Ofir Nachum • Mohammad Norouzi • Dale Schuurmans	2016-11-28
Reinforcement Learning through Asynchronous Advantage Actor-Critic on a GPU \| Mohammad Babaeizadeh • Iuri Frosio • Stephen Tyree • Jason Clemons • Jan Kautz	2016-11-18
Sample Efficient Actor-Critic with Experience Replay \| Ziyu Wang • Victor Bapst • Nicolas Heess • Volodymyr Mnih • Remi Munos • Koray Kavukcuoglu • Nando de Freitas	2016-11-03
Asynchronous Methods for Deep Reinforcement Learning \| Volodymyr Mnih • Adrià Puigdomènech Badia • Mehdi Mirza • Alex Graves • Timothy P. Lillicrap • Tim Harley • David Silver • Koray Kavukcuoglu # 5 ATARI GAMES ON ATARI 2600 ROAD RUNNER	2016-02-04

Tasks

TASK	PAPERS	SHARE
Continuous Control	23	15.75%
Atari Games	16	10.96%
Image Classification	6	4.11%
Multi-agent Reinforcement Learning	6	4.11%
Object Detection	6	4.11%
Domain Adaptation	6	4.11%
Decision Making	6	4.11%
Semantic Segmentation	5	3.42%
adversarial training	5	3.42%

Usage Over Time

This feature is experimental; we are continuously improving our matching algorithm.

Components

COMPONENT	TYPE	Add Remove
🤖 No Components Found	You can add them if they exist; e.g. Mask R-CNN uses RoIAlign

Categories

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Regularization