Robot Learning: Agentic and Autonomous Systems - Part 2

Overview

Join us in understanding the dynamics of autonomous learning agents and the importance of their careful design for successful interaction. This course bridges the gap between autonomous systems and agentic models, providing insights into real-world reinforcement learning applications.

Offered by YouTube University under the categories of Artificial Intelligence Courses and Computer Science Courses, this session is a part of an educational series diving deep into modern technologies.

Syllabus

• Introduction to Agentic and Autonomous Systems
Overview of autonomous learning agents
Agentic models in reinforcement learning
• Reinforcement Learning Foundations
Recap of core concepts from Part 1
Policy gradients and advanced policy optimization
• Design Principles for Autonomous Agents
Architectures for agent decision-making
Exploration vs. exploitation in autonomous systems
• Real-World Challenges and Solutions
Handling partial observability
Dealing with non-stationary environments
• Interactive and Adaptive Systems
Frameworks for agent-system interactions
Online and lifelong learning approaches
• Safety and Ethics in Autonomous Systems
Ethical considerations in autonomous decision-making
Ensuring reliability and robustness in operation
• Case Studies of Autonomous Learning Agents
Real-world applications and deployment case studies
Analysis of success stories and failures
• Advanced Topics in Robot Learning
Multi-agent systems and collaboration
Transfer learning and domain adaptation
• Project and Practical Implementation
Designing and evaluating a reinforcement learning agent
Hands-on experience with simulation tools and environments
• Future Trends in Autonomous and Agentic Systems
Advances in theory and practice
Emerging technologies and future directions in robot learning

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Subjects

Computer Science