Reinforcement Learning in Robotics | IABAC

1. Reinforcement Learning in Robotics iabac.org

2. Introduction to Reinforcement Learning RL is a type of machine learning where an agent learns by interacting with an environment. The goal is to maximize rewards through trial and feedback. Commonly used in autonomous systems like robots, drones, and self-driving cars. iabac.org

3. Key Concepts in RL Agent: The learner or decision-maker (robot). Environment: The space where the agent operates. Actions: Moves or operations performed by the agent. Rewards: Feedback that guides the agent’s learning. Policy: Strategy used to decide actions. iabac.org

4. RL in Robotics Robots learn to perform tasks through repeated trial and error. RL enables adaptability in uncertain environments. Common robotic applications: Path planning and obstacle avoidance Robotic arm control Grasping and object manipulation iabac.org

5. Popular RL Algorithms in Robotics Q-Learning: Basic value-based learning method. Deep Q-Networks (DQN): Combines deep learning with RL. Policy Gradient Methods: Used for continuous control tasks. Proximal Policy Optimization (PPO): Balances performance and stability. iabac.org

6. Future and Challenges Challenges Future Trends Simulation-to-real transfer learning Multi-agent reinforcement learning Integration with generative AI High computational costs Slow training in real environments Safety and stability concerns iabac.org

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