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🏓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.
The simulation of Epsilon-Greedy and Thompson Sampling algorithms for Bayesian A/B Testing. The project shows how both algorithms find the optimal bandit and approximate the rewards of each bandit, given the true reward. Visualizations are done to demonstrate the learning process and convergence.
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.