Chrome Dino Run using Reinforcement Learning

08/15/2020 ∙ by Divyanshu Marwah, et al. ∙ 0 ∙ share

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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Chrome Dino Run using Reinforcement Learning

Authors

FlapAI Bird: Training an Agent to Play Flappy Bird Using Reinforcement Learning Techniques

Reinforcement Learning and Video Games

Double Q(σ) and Q(σ, λ): Unifying Reinforcement Learning Control Algorithms

Successor Features Support Model-based and Model-free Reinforcement Learning

Performing Deep Recurrent Double Q-Learning for Atari Games

Deterministic limit of temporal difference reinforcement learning for stochastic games

Reinforcement Learning for Electricity Network Operation

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