An OpenAIGym-based framework allowing to test Delay-Aware Deep Reinforcement Learning algorithms for cooperative multi-UAV systems in fully customizable scenarios (e.g., coverage maximization, tracking, spotting).
Experience the fusion of finance and artificial intelligence with this cutting-edge project that implements a deep reinforcement learning agent for automated stock trading. Leveraging advanced neural network architectures and a meticulously crafted reward function, the agent learns to make profitable trading decisions in a simulated environment
The Repository contains multiple Project on Reinforcement Learning.
Unsupervised Learning on OpenAI Gym to understand the properties of such algorithms, using Value Iteration and Q-Learning algorithmn. Developed with my coding friend @jibrilsharafi.
PyTorch implementation of Proximal Policy Optimization for continuous pendulum control, featuring GAE, clipped objectives, training curves, and value-function visualization.
Vision-based intelligent game agent that learns to interact with a game environment using deep reinforcement learning. The system relies solely on raw visual input (screen pixels) to perceive the environment and make decisions, mimicking human-like perception and action.
The project is for gym simulation, maintained at CCSL Lab in National Sun Yat-sen University (NSYSU) CSE Department
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