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Final Year Project at UAM. study and develop of a fire monitoring system using drones.Using a Unity3D enviroment and reinforce learning to train the agents to track the fire expansion and obtain information with a high degree of reliability with respect to the real fire growth.
Reinforcement learning approach to physics based car which learns to avoid obstacles. Project was made in Unity, using MLAgents, reinfercement learning and C# scripts. The car recieves positive reward for driving into the goal and negative reward for driving into the walls, obstacles or for circling around.
MLAgents project enables the creation of AI agents using reinforcement learning techniques. It features a pre-configured mechanism for agents and a gaming environment where they learn and perform tasks.
Most important scripts from my final degree project using MLAgents (Deep Reinforcement Learning) and Unity. It also includes a traditional AI algorithm to be compared with the ML one. Both AIs (ML and heuristic) had very similar results in a bird race, both AI methods are valid for this type of game.
Academic project for COGS 300. Utilizing ML-Agents, an AI tool for the Unity game engine, my team and I programmed a robot to fight in the class competition. Fifth place.