Using Generative Adversarial Nets on Atari Games for Feature Extraction in Deep Reinforcement Learning

Aydın, Ayberk; Sürer, Elif

doi:10.1109/siu49456.2020.9302454

2020 28th Signal Processing and Communications Applications Conference (SIU) 2020

DOI: 10.1109/siu49456.2020.9302454

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Using Generative Adversarial Nets on Atari Games for Feature Extraction in Deep Reinforcement Learning

Ayberk Aydın¹,

Elif Sürer

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Transition Based Discount Factor for Model Free Algorithms in Reinforcement Learning

Sharma

Gupta²,

Lakshmanan

et al. 2021

Symmetry

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Reinforcement Learning (RL) enables an agent to learn control policies for achieving its long-term goals. One key parameter of RL algorithms is a discount factor that scales down future cost in the state’s current value estimate. This study introduces and analyses a transition-based discount factor in two model-free reinforcement learning algorithms: Q-learning and SARSA, and shows their convergence using the theory of stochastic approximation for finite state and action spaces. This causes an asymmetric discounting, favouring some transitions over others, which allows (1) faster convergence than constant discount factor variant of these algorithms, which is demonstrated by experiments on the Taxi domain and MountainCar environments; (2) provides better control over the RL agents to learn risk-averse or risk-taking policy, as demonstrated in a Cliff Walking experiment.

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Transition Based Discount Factor for Model Free Algorithms in Reinforcement Learning

Sharma

Gupta²,

Lakshmanan

et al. 2021

Symmetry

View full text Add to dashboard Cite

show abstract

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Using Generative Adversarial Nets on Atari Games for Feature Extraction in Deep Reinforcement Learning

Cited by 1 publication

References 0 publications

Transition Based Discount Factor for Model Free Algorithms in Reinforcement Learning

Transition Based Discount Factor for Model Free Algorithms in Reinforcement Learning

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