Reinforcement Learning for Autonomous Systems Virtual Internship

In this task, students will implement a basic reinforcement learning agent to solve a simple grid-world environment. They will design the agent's state representation, define the reward function, and train the agent using a basic RL algorithm.

In this task, students will design and train a deep reinforcement learning agent to control a robotic manipulator for object grasping and manipulation tasks. They will integrate the RL agent with a robotic simulation environment and evaluate its performance.

In this task, students will design and implement an RL-based navigation system for a mobile robot to navigate through a complex environment with obstacles and dynamic elements. They will integrate the RL agent with sensor data, path planning, and control systems, and evaluate its performance in simulation.

In this capstone task, students will take one of their previous RL-based autonomous systems (e.g., the robot manipulator or the mobile robot navigation system) and develop a plan for its deployment and optimization in a real-world scenario. They will address key deployment challenges and implement strategies for performance monitoring and continuous improvement.