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This project implements Value Iteration and Q-Learning algorithms to solve a variety of gridworld mazes and puzzles. It provides pre-defined policies that can be customized by adjusting parameters and policy optimization through iterative reinforcement learning. It also brings exploration capabilities to the agent with Epsilon Greedy Q-Learning.
🏓Deep learning model is presented to successfully learn control policies directly from high-dimensional sensory input using reinforcement learning. The model is a convolutional neural network, trained with a variant of Q-learning, whose input is raw pixels and whose output is a value function estimating future rewards in RL Pong environment.