Overview
Explore how autonomous learning agents require careful design to achieve effective interaction, connecting autonomous systems with agentic models for real-world reinforcement learning.
Syllabus
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- 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
Taught by
Tags
On-Demand
