Training AI to play Atari using OpenAI Gym and Deep Reinforcement Learning
For this task, I used the openai gym framework that renders the atari environment and exposes APIs to the game's world.
My agent was a Deep Q-learning agent that takes in sequences of 4 images of the game's world as inputs and learns the Q functions for several actions in any given state (sequence of 4 images of the game). These actions include left, right, fire.
The agent used a deep learning architecture consisting of 3 convolution layers and one dense layer. The agent trained using target networks and experience replay buffers to prevent the propagation of errors in learning across its deep learning stack.
For this task, I used the openai gym framework that renders the atari environment and exposes APIs to the game’s world.
My agent was a Deep Q-learning agent that takes in sequences of 4 images of the game’s world as inputs and learns the Q functions for several actions in any given state (sequence of 4 images of the game). These actions include left, right, fire.
The agent used a deep learning architecture consisting of 3 convolution layers and one dense layer. The agent trained using target networks and experience replay buffers to prevent the propagation of errors in learning across its deep learning stack.
