A survey on model-based reinforcement learning

Analysis

Why This Paper Matters

Reinforcement learning (RL) has achieved remarkable successes in domains like games and robotics, but its reliance on large amounts of environment interaction remains a critical bottleneck. Model-based reinforcement learning (MBRL) offers a promising paradigm to address this by learning a model of the environment's dynamics, enabling agents to plan and simulate experiences without direct interaction. This survey is timely as MBRL has gained increasing attention, yet the field is fragmented with diverse approaches. By providing a structured overview, the paper helps researchers navigate the landscape and understand the core ideas that drive sample efficiency gains.

The significance of this survey lies in its potential to accelerate progress in RL by consolidating knowledge. For practitioners, it offers a roadmap for selecting appropriate MBRL techniques based on their problem constraints. For researchers, it highlights open challenges, such as model bias and uncertainty handling, which are crucial for deploying RL in real-world scenarios where interactions are costly or risky.

Technical Contributions

The survey categorizes MBRL methods along several dimensions:

• Model types: Distinguishes between models that learn the transition dynamics, reward functions, or both.
• Model usage: Explores how models are used for planning (e.g., model predictive control), policy optimization (e.g., imagination-based training), or both.
• Model learning: Discusses approaches for learning models, including probabilistic models, ensembles, and latent-space models.
• Handling model error: Reviews techniques to mitigate the impact of model inaccuracies, such as uncertainty estimation and model-based policy optimization.

These categorizations provide a clear framework for understanding the design choices in MBRL and their implications for sample efficiency and performance.

Results

As a survey, the paper does not introduce new experimental results. Instead, it synthesizes findings from prior studies, noting that MBRL methods can achieve comparable or better performance than model-free counterparts with significantly fewer environment interactions. For example, in continuous control benchmarks, MBRL algorithms have demonstrated sample efficiency improvements of several orders of magnitude. However, the survey also notes that MBRL often lags behind model-free methods in final performance due to model bias, and that achieving both high sample efficiency and asymptotic performance remains a challenge.

Significance

The broader impact of this survey is to consolidate the state of the art in MBRL, making it more accessible to the AI community. By clarifying the strengths and weaknesses of different approaches, it can guide future research toward more robust and scalable MBRL methods. This is particularly important for applying RL to real-world problems where data collection is expensive or dangerous, such as autonomous driving, healthcare, and robotics. The survey also underscores the importance of sample efficiency as a key metric for RL research, potentially shifting focus from raw performance to practical applicability.