Emergence of Multi-Agent AI Systems

Introduction

• New class of AI: Robots enhanced with AI capabilities.
• Language serves as an input for these systems.

Training Complex AI Systems

Initial Frameworks

• Early development involved:
  • Large language models based on Transformer architecture.
  • Three phases of training:
    • Pre-training
    • Fine-tuning (supervised)
    • Alignment (reinforcement learning from human feedback)

Evolution of Models

• Shift to Vision-Language Models:
  • Introduction of diffusion models and advanced training techniques.
  • Development of Diffusion Transformers.
• Transition to Video Language Models.

Current Research Trends

Vision Language Action Models

• Recent focus on Vision Language Action Models.
• Back to diffusion models as the main architecture for training:
  • Pre-training with imitation learning and behavior cloning.
  • Fine-tuning with reinforcement learning.

Importance of Imitation Learning

• Imitation learning allows agents to learn tasks by mimicking expert behavior (e.g., human-guided demonstrations).
• Avoids starting from scratch in training:
  • Real demonstrations provided by experts or virtual agents.

Addressing Challenges in Robotics

Precision and Accuracy

• Challenges in precision for delicate operations addressed through:
  • Residual Reinforcement Learning on top of frozen models.
• Domain shift problem tackled with predicted reward fine-tuning.

Markov Decision Processes (MDP)

Key Concepts

• Utilization of Markov Decision Processes in AI training.
  • Definition and regularization of MDPs.
  • Development of algorithms for optimization problems in reinforcement learning.

Multi-Agent Systems

Coordination of Multiple Agents

• Focus on health-related applications using multiple robotic arms.
• Multi-agent systems require coordination among agents to perform complex tasks.
• Application of Science and advanced methodologies over trial and error.

Game Theory in Multi-Agent Systems

• Importance of Game Theory in coordinating actions of multiple agents:
  • Games extend classical MDPs with joint actions and rewards.
  • Future focus on integrating Game Theory into reinforcement and imitation learning phases.

Conclusion

• Introduction of a new YouTube playlist:
  • "Imitation Learning of Agent Advanced ORL": Covers both single and multi-agent imitation learning.
• Upcoming video on the details of Markov Game Theory.