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FlapAI Bird: Training an Agent to Play Flappy Bird Using Reinforcement Learning Techniques
Reinforcement learning is one of the most popular approach for automated...
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Reinforcement Learning and Video Games
Reinforcement learning has exceeded human-level performance in game play...
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Double Q(σ) and Q(σ, λ): Unifying Reinforcement Learning Control Algorithms
Temporal-difference (TD) learning is an important field in reinforcement...
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Successor Features Support Model-based and Model-free Reinforcement Learning
One key challenge in reinforcement learning is the ability to generalize...
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Performing Deep Recurrent Double Q-Learning for Atari Games
Currently, many applications in Machine Learning are based on define new...
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Deterministic limit of temporal difference reinforcement learning for stochastic games
Reinforcement learning in multi-agent systems has been studied in the fi...
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Reinforcement Learning for Electricity Network Operation
This paper presents the background material required for the Learning to...
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Chrome Dino Run using Reinforcement Learning
Reinforcement Learning is one of the most advanced set of algorithms known to mankind which can compete in games and perform at par or even better than humans. In this paper we study most popular model free reinforcement learning algorithms along with convolutional neural network to train the agent for playing the game of Chrome Dino Run. We have used two of the popular temporal difference approaches namely Deep Q-Learning, and Expected SARSA and also implemented Double DQN model to train the agent and finally compare the scores with respect to the episodes and convergence of algorithms with respect to timesteps.
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