THIS IS THE Multi_Agent_Learning_PPT.pptx

MULTI AGENT

1. Multi-Agent Learning An Overview of Learning in Multi-Agent Systems Your Name | Course Info

2. Introduction • Multi-Agent Learning (MAL) = agents learning in environments with multiple interacting agents • Combines Reinforcement Learning and Game Theory • Applied in AI, robotics, economics, and distributed systems

3. Why Multi-Agent Learning? • Real-world problems often involve multiple decision makers • Agents must adapt to dynamic environments • Enables cooperation, competition, and coordination

4. Key Concepts • Agent: An autonomous decision-making entity • Environment: Dynamic system influenced by multiple agents • Interaction Types: - Cooperative - Competitive - Mixed

5. Learning Paradigms 1. Independent Learning: Each agent learns individually 2. Joint Action Learning: Agents consider others’ actions 3. Centralized Training with Decentralized Execution (CTDE)

6. Approaches • Reinforcement Learning (Q-Learning, SARSA, DDPG, PPO) • Evolutionary Learning • Opponent Modeling • Communication-based Learning • Imitation & Transfer Learning

7. Challenges • Non-stationarity: Environment changes as agents learn • Scalability: Many agents increase complexity • Credit Assignment Problem • Exploration vs Exploitation dilemma

8. Applications • Autonomous driving (vehicle coordination) • Robotics (multi-robot systems) • Smart grids (energy optimization) • Economics & auctions • Online multiplayer games • Traffic management

9. Advantages • Enables adaptive decision-making • Models real-world multi-actor interactions • Encourages emergence of cooperation and competition

10. Limitations • High computational cost • Learning instability due to dynamic interactions • Coordination failures possible

11. Case Study Example • Example: Multi-Agent Reinforcement Learning in traffic lights control • Each light = agent • Learns to minimize congestion collaboratively

12. Future Directions • Explainable Multi-Agent Learning • Human-AI Collaboration • Combining symbolic reasoning with MAL • Large-scale simulation environments

13. References • Shoham, Y., & Leyton-Brown, K. (2009). Multiagent Systems. • Busoniu, L., Babuska, R., & De Schutter, B. (2008). A Comprehensive Survey of Multi-Agent Reinforcement Learning. • Weiss, G. (Ed.). (2013). Multiagent Systems (2nd ed.). MIT Press.