MOReL : Model-Based Offline Reinforcement Learning (2005.05951v3)

Abstract: In offline reinforcement learning (RL), the goal is to learn a highly rewarding policy based solely on a dataset of historical interactions with the environment. The ability to train RL policies offline can greatly expand the applicability of RL, its data efficiency, and its experimental velocity. Prior work in offline RL has been confined almost exclusively to model-free RL approaches. In this work, we present MOReL, an algorithmic framework for model-based offline RL. This framework consists of two steps: (a) learning a pessimistic MDP (P-MDP) using the offline dataset; and (b) learning a near-optimal policy in this P-MDP. The learned P-MDP has the property that for any policy, the performance in the real environment is approximately lower-bounded by the performance in the P-MDP. This enables it to serve as a good surrogate for purposes of policy evaluation and learning, and overcome common pitfalls of model-based RL like model exploitation. Theoretically, we show that MOReL is minimax optimal (up to log factors) for offline RL. Through experiments, we show that MOReL matches or exceeds state-of-the-art results in widely studied offline RL benchmarks. Moreover, the modular design of MOReL enables future advances in its components (e.g. generative modeling, uncertainty estimation, planning etc.) to directly translate into advances for offline RL.

• The paper introduces a novel model-based framework that builds a pessimistic MDP from offline datasets to mitigate distribution shift.
• The methodology uses an Unknown State-Action Detector to partition state-action space, penalizing uncertain regions for stable policy learning.
• Empirical results show MOReL achieves near-minimax optimality and outperforms existing offline RL methods on continuous control benchmarks.

Overview of MOReL: Model-Based Offline Reinforcement Learning

The paper "MOReL: Model-Based Offline Reinforcement Learning" introduces a framework for offline reinforcement learning (RL) that leverages model-based approaches to optimize policy learning from offline datasets. The proposed methodology, MOReL, aims to address distinctive challenges associated with offline RL, such as distribution shift and model exploitation.

At its core, MOReL tackles the problem of learning an effective policy using only pre-collected, static datasets, which is crucial for applications where online exploration is constrained or unsafe. The paper suggests a two-step procedure within the MOReL framework: constructing a pessimistic Markov Decision Process (P-MDP) and learning a near-optimal policy within this P-MDP.

MOReL Framework and Pessimistic MDP

MOReL involves learning an approximate dynamics model from the offline dataset, followed by a partitioning of the state-action space into "known" and "unknown" regions using an Unknown State-Action Detector (USAD). The P-MDP penalizes excursions into unknown regions by assigning a significant negative reward, thus regularizing the learned policy to avoid reliance on parts of the state space where the model is uncertain or inaccurate. This construction aims to safeguard against issues like model exploitation.

Theoretical Implications

Theoretical analysis demonstrates that MOReL achieves near-minimax optimality (up to logarithmic factors) and provides rigorous upper and lower bounds on policy sub-optimality. These bounds critically depend on the distribution overlap between the collected data and the optimal policy, as well as other factors such as the accuracy of the transition model in known regions.

Empirical Results

Experimentally, MOReL is evaluated against established offline RL algorithms such as BCQ, BEAR, and recent variants of BRAC across several continuous control benchmarks. The results indicate that MOReL outperforms existing methods in a majority of tasks, establishing state-of-the-art performance in many test scenarios. This superior performance is attributed to MOReL's ability to better handle distribution shifts by its innovative P-MDP construct.

Impact and Future Directions

The introduction of pessimism via P-MDPs in MOReL expands the possibilities for applying RL in domains where dynamic interaction is prohibitive. Its modular framework suggests potential adaptability and improvements with advancements in components like model learning and planning algorithms.

For future advancements, exploration of MOReL's application beyond benchmark tasks into real-world problems is promising. Additionally, investigation into extensions allowing greater generalizations or efficiently handling more complex state spaces could further enhance its applicability.

In summary, MOReL offers a robust approach to offline RL by integrating pessimism into the exploration-exploitation trade-off within model-based learning. Its contribution lies in demonstrating how effective policy learning can be attained even when direct environment interaction is off-limits, a vital step forward in the broader reinforcement learning discourse.