RobotLearning - Agentic and Autonomous Systems

Overview

Explore the design and tooling of autonomous learning agents, connecting autonomous systems with agentic models to achieve effective real-world reinforcement learning.

Syllabus

• Introduction to Autonomous Learning Agents
Overview of Autonomous Systems
Definitions and Characteristics of Autonomous Learning Agents
Historical Context and Evolution
• Foundations of Reinforcement Learning
Key Concepts: Agents, Environments, Rewards, and State-Action Pairs
Exploration vs. Exploitation Dilemma
Basic Algorithms: Q-Learning, SARSA
• Agentic Models in Autonomous Systems
Defining Agentic Models
Comparison with Other Models
Applications in Autonomous Systems
• Designing Autonomous Learning Agents
Components of Autonomous Agents
Architectural Considerations
Designing Agent-Based Solutions
• Reinforcement Learning in Autonomous Systems
Deep Reinforcement Learning
Policy Gradients and Actor-Critic Methods
Case Studies: Applications in Robotics and Other Fields
• Tools and Platforms for Developing Learning Agents
Frameworks: TensorFlow Agents, OpenAI Gym
Simulation Environments: ROS, Gazebo
Practical Implementation Challenges
• Advanced Topics in Autonomous Learning and Control
Multi-Agent Reinforcement Learning
Hierarchical Reinforcement Learning
Safety and Ethical Considerations
• Connecting Agentic Models with Real-World Applications
Case Studies of Successful Implementations
Integration Challenges and Solutions
Future Trends in Agentic and Autonomous Systems
• Course Wrap-Up and Project
Review of Key Concepts
Final Project: Design and Simulate an Autonomous Learning Agent
Discussion on Emerging Areas in RobotLearning

Subjects

Computer Science