Product Information
Description
World Models and Continual Learning: How Adaptive Agents Remember and Forget — a survey of a frontier of autonomous AI in the Agents in the Wild series.
Book framework (chapter themes):
- Introduction — The Intersection of World Models and Continual Learning, from Cognitive Science to Artificial Intelligence
- Basic Paradigms of Continual Learning and the Problem of Catastrophic Forgetting
- Mathematical Foundations of World Models — From Cognitive Representation to Predictive Coding
- World Model Architecture Design — Discretization, Continuous Latent Spaces, and Hybrid Representations
- World Models in Continual Reinforcement Learning: Knowledge Retention and Task Adaptation
- Generative Replay and Dreaming Mechanisms: World-Model-Driven Memory Consolidation
- Online Continual Learning: Data Streams, Concept Drift, and Real-Time Adaptation
- The Theoretical Connections Among Transfer Learning, Domain Adaptation, and Continual Learning
- Meta-Learning and Continual Learning: Learning to Learn and Fast Adaptation
- Regularization-Based Continual Learning Methods: From Elastic Weight Consolidation to Parameter-Efficient Fine-Tuning
- Replay-Based Continual Learning: Experience Replay, Buffer Management, and Selective Retention
- Architectural-Expansion-Based Continual Learning: Subspaces, Modularity, and Parameter Growth
- Federated Continual Learning: Knowledge Retention and Privacy Protection in Distributed Environments
- World Models in Continual Learning for Robotics: From Simulation to the Real World
- Multimodal Continual Learning: Adapting Joint Vision-Language-Action Representations
- Continual Learning Benchmarks and Evaluation Systems: From Artificial Sequences to Real-World Data
- Continual Learning in the Era of Large Language Models and Foundation Models
- Theoretical Analysis in Continual Learning: Statistical Physics, Optimal Control, and Generalization Bounds
- Open Challenges and Future Directions: Toward Truly Adaptive Agents
- Conclusion: An Outlook for a Unified Framework of World Models and Continual Learning
Who it is for. Researchers, engineers, and advanced enthusiasts in the field of artificial intelligence, with a foundation in machine learning, who wish to systematically understand the intersection of world models and continual learning..
