Reinforcement Learning for Autonomous Robot Navigation

[ayush-09]

💥 Proposal

The goal of this project is to develop an autonomous robot navigation system using reinforcement learning. The robot will learn to navigate and explore its environment efficiently without any explicit programming by using a reinforcement learning algorithm. The project will focus on training the robot to navigate a maze-like environment and reach predefined goal locations.

Environment Setup:

Create a simulated environment resembling a maze-like structure with obstacles and goal locations. Define the robot's movement capabilities and constraints within the environment.

Reinforcement Learning Algorithm:

Select a suitable reinforcement learning algorithm (e.g., Q-Learning, Deep Q-Network, or Proximal Policy Optimization). Develop an agent that interacts with the environment and learns from the rewards received. Implement exploration-exploitation strategies to balance learning and exploitation.

State Representation and Reward Structure:

Design an appropriate state representation that captures relevant information about the environment. Define a reward structure that encourages the robot to navigate efficiently and reach the goal locations.

Training and Evaluation:

Train the reinforcement learning agent using the defined environment, state representation, reward structure, and learning algorithm. Monitor the agent's progress and evaluate its performance by measuring success rates, navigation efficiency, and learning convergence. Iterate and optimize the training process to achieve better results.

[khusheekapoor]

@ayush-09 - you can go ahead! We are assigning you 21 days for this project, after which it will be assigned to someone else if not completed. All the best!