Performing Deep Recurrent Double Q-Learning for Atari Games
원문 초록
Currently, many applications in Machine Learning are based on define new models to extract more information about data, In this case Deep Reinforcement Learning with the most common application in video games like Atari, Mario, and others causes an impact in how to computers can learning by himself with only information called rewards obtained from any action. There is a lot of algorithms modeled and implemented based on Deep Recurrent Q-Learning proposed by DeepMind used in AlphaZero and Go. In this document, We proposed Deep Recurrent Double Q-Learning that is an implementation of Deep Reinforcement Learning using Double Q-Learning algorithms and Recurrent Networks like LSTM and DRQN.
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Generalized Adaptive Transfer Network: Enhancing Transfer Learning in Reinforcement Learning Across Domains
Task Independent Capsule-Based Agents for Deep Q-Learning
Deep Reinforcement Learning Based Power Allocation for D2D Network
Deep Reinforcement Learning with Gradient Eligibility Traces
Adaptive Action Duration with Contextual Bandits for Deep Reinforcement Learning in Dynamic Environments
DR-SAC: Distributionally Robust Soft Actor-Critic for Reinforcement Learning under Uncertainty
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