Chrome Dino Run using Reinforcement Learning
This is an incremental application of existing RL methods to a specific game, with no broader impact claimed.
The paper applied model-free reinforcement learning algorithms, including Deep Q-Learning, Expected SARSA, and Double DQN, to train an agent for playing Chrome Dino Run, comparing their scores and convergence over episodes and timesteps.
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.