Description:
This project implements a Reinforcement Learning (RL) agent to play the classic Super Mario Bros game. The agent is trained to navigate through the game's environment using deep learning techniques.
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Description:
This project explores reinforcement learning algorithms applied to solving maze navigation tasks. It involves implementing algorithms such as Q-learning or Deep Q-Networks (DQN) to find optimal paths through maze environments.
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Description:
The Cartpole project focuses on the classic control problem where an inverted pendulum (pole) must be balanced on top of a moving cart. Reinforcement learning techniques are used to train an agent to keep the pole balanced by controlling the cart's movements.
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Description:
This project develops an automated trading bot that utilizes machine learning for sentiment analysis to inform trading decisions. It integrates with the Alpaca API for trade execution and uses FinBERT for sentiment estimation from news headlines.
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