Model-based Reinforcement Learning: A Survey (2006.16712v4)

Abstract: Sequential decision making, commonly formalized as Markov Decision Process (MDP) optimization, is a important challenge in artificial intelligence. Two key approaches to this problem are reinforcement learning (RL) and planning. This paper presents a survey of the integration of both fields, better known as model-based reinforcement learning. Model-based RL has two main steps. First, we systematically cover approaches to dynamics model learning, including challenges like dealing with stochasticity, uncertainty, partial observability, and temporal abstraction. Second, we present a systematic categorization of planning-learning integration, including aspects like: where to start planning, what budgets to allocate to planning and real data collection, how to plan, and how to integrate planning in the learning and acting loop. After these two sections, we also discuss implicit model-based RL as an end-to-end alternative for model learning and planning, and we cover the potential benefits of model-based RL. Along the way, the survey also draws connections to several related RL fields, like hierarchical RL and transfer learning. Altogether, the survey presents a broad conceptual overview of the combination of planning and learning for MDP optimization.

• The paper highlights how integrating planning with learning enhances sample efficiency using techniques like state abstraction and uncertainty handling.
• It examines key challenges in model learning, including stochasticity, uncertainty, partial observability, and non-stationarity, proposing robust adaptation strategies.
• The survey explores end-to-end differentiable methods such as implicit models and value-equivalent planning to optimize decision-making in MDPs.

Model-based Reinforcement Learning: A Survey

Introduction to Model-based Reinforcement Learning

Model-based reinforcement learning (RL) addresses sequential decision-making by integrating planning with reinforcement learning in the context of Markov Decision Processes (MDPs). Unlike model-free RL, model-based approaches leverage an explicit model of the environment's dynamics. This survey focuses on categorizing model-based RL, discussing model learning challenges, and exploring the integration of planning and learning.

Model Learning in Model-based RL

Model learning is crucial for model-based RL, entailing the approximation of the environment's dynamics. Key challenges in model learning include:

• Stochasticity: Handling stochastic transitions requires models that can predict distributions over possible next states.
• Uncertainty: Addressing epistemic uncertainty with Bayesian or frequentist methods is vital for robust planning.
• Partial Observability: Techniques like recurrent neural networks (RNNs) and external memory systems help mitigate the effects of incomplete state information.
• Non-stationarity: Adapting models to changing dynamics is essential for maintaining performance.
• State and Temporal Abstraction: Leveraging representation learning to create compact state representations and abstract actions can improve model efficiency.

Integration of Planning with Learning

The integration of planning and learning in model-based RL involves several considerations:

• Start State for Planning: Choosing whether to initiate planning from uniform, visited, prioritized, or current states can impact exploration efficiency.
• Budget Allocation: Balancing between planning iterations and real environment interactions is crucial for optimizing sample efficiency.
• Planning Methodology: Deciding on forward or backward planning, breadth vs. depth of search, and handling uncertainty (e.g., data-close planning or uncertainty propagation) are central to effective planning.
• Learning and Acting Loop: Using planning results to update value/policy functions and guide real-world actions can enhance learning stability and performance.

Implicit Model-based Reinforcement Learning

Implicit model-based RL involves optimizing elements of the model and planning process through end-to-end differentiability. This approach focuses on:

• Value Equivalent Models: These models aim to predict value-relevant characteristics rather than complete state predictions. Examples include MuZero (2006.16712) and Value Iteration Networks.
• Learning to Plan: Here, the planning operations themselves are optimized, often using algorithmic function approximation to improve policy improvement capabilities.
• Combined Approaches: Jointly optimizing both the transition model and the planning procedure can create a comprehensive end-to-end framework, although it presents optimization challenges.

Benefits of Model-based RL

Model-based RL offers several advantages, including:

• Data Efficiency: It can significantly reduce real-world sample complexity by effectively using model samples.
• Exploration: Two-phase exploration and intrinsic motivation drive targeted exploration strategies.
• Optimality and Stability: It has the potential for superior asymptotic performance due to the combination of planning and global approximation.
• Transfer Learning: Model-based RL efficiently adapts to new tasks by transferring learned dynamics.
• Safety and Explainability: Models provide a foundation for safe exploration and explainable decision-making.

Conclusion

Model-based reinforcement learning represents a powerful approach to MDP optimization by integrating planning with learning. This survey provided a detailed categorization, discussed the challenges and integration strategies, and highlighted future research directions. Model-based RL stands to benefit from continued advancements in model learning, planning methodologies, and optimization techniques, promising enhanced performance and applicability across diverse domains.