Maximum Entropy Dueling Network Architecture in Atari Domain
Alireza Nadali, Mohammad Mehdi Ebadzadeh

TL;DR
This paper introduces an enhanced deep reinforcement learning architecture for Atari games that combines Dueling Networks with Maximum Entropy principles, leading to improved policy evaluation.
Contribution
It presents a novel architecture integrating Maximum Entropy with Dueling Networks, enhancing value estimation in Atari domain reinforcement learning.
Findings
Better policy evaluation performance in Atari games
Outperforms original Dueling Networks and other value-based methods
Demonstrates the effectiveness of Maximum Entropy integration
Abstract
In recent years, there have been many deep structures for Reinforcement Learning, mainly for value function estimation and representations. These methods achieved great success in Atari 2600 domain. In this paper, we propose an improved architecture based upon Dueling Networks, in this architecture, there are two separate estimators, one approximate the state value function and the other, state advantage function. This improvement based on Maximum Entropy, shows better policy evaluation compared to the original network and other value-based architectures in Atari domain.
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Taxonomy
TopicsViral Infectious Diseases and Gene Expression in Insects · Reinforcement Learning in Robotics · Evolutionary Algorithms and Applications
