MOPO: Model-based Offline Policy Optimization (2005.13239v6)

Abstract: Offline reinforcement learning (RL) refers to the problem of learning policies entirely from a large batch of previously collected data. This problem setting offers the promise of utilizing such datasets to acquire policies without any costly or dangerous active exploration. However, it is also challenging, due to the distributional shift between the offline training data and those states visited by the learned policy. Despite significant recent progress, the most successful prior methods are model-free and constrain the policy to the support of data, precluding generalization to unseen states. In this paper, we first observe that an existing model-based RL algorithm already produces significant gains in the offline setting compared to model-free approaches. However, standard model-based RL methods, designed for the online setting, do not provide an explicit mechanism to avoid the offline setting's distributional shift issue. Instead, we propose to modify the existing model-based RL methods by applying them with rewards artificially penalized by the uncertainty of the dynamics. We theoretically show that the algorithm maximizes a lower bound of the policy's return under the true MDP. We also characterize the trade-off between the gain and risk of leaving the support of the batch data. Our algorithm, Model-based Offline Policy Optimization (MOPO), outperforms standard model-based RL algorithms and prior state-of-the-art model-free offline RL algorithms on existing offline RL benchmarks and two challenging continuous control tasks that require generalizing from data collected for a different task. The code is available at https://github.com/tianheyu927/mopo.

Overview of "MOPO: Model-based Offline Policy Optimization"

The paper "MOPO: Model-based Offline Policy Optimization" introduces an approach to address the challenges in offline reinforcement learning (RL), where the aim is to learn effective policies exclusively from previously collected data without additional interaction with the environment. The proposed algorithm, Model-based Offline Policy Optimization (MOPO), addresses the issue of distributional shift inherent in offline RL by leveraging model-based approaches.

Core Contributions

The primary contribution of this work is the development of MOPO, which incorporates a reward penalty based on the uncertainty of dynamics models to enable more robust generalization in offline settings. The key insight is that model-based methods can allow policies to generalize to states and actions outside the data distribution without the instability issues observed in model-free approaches.

Theoretical Foundation

MOPO is grounded in rigorous theoretical analysis. The authors derive a method for quantifying the uncertainty of the learned model and formulate an uncertainty-penalized Markov Decision Process (MDP). This leads to a theoretical guarantee: the algorithm optimizes a policy that maximizes a lower bound of the expected return in the true MDP. The formulation provides a mechanism to trade off between the returns and the risks associated with unobserved states and actions.

Empirical Performance

Empirical results demonstrate MOPO’s superior performance on existing offline RL benchmarks against state-of-the-art model-free algorithms, such as BEAR and BRAC. The paper presents a thorough evaluation on both standard datasets from D4RL and customized environments where generalization to out-of-distribution states is necessary. Specifically, MOPO significantly outperforms competing methods on tasks requiring extrapolation beyond the behavior policy’s data support, indicating its capability to effectively utilize the dynamics model for policy optimization.

Practical Implications and Future Directions

Practically, MOPO provides a robust framework for offline RL that can be applied to domains with pre-existing datasets, such as autonomous driving and healthcare, where policy safety and robustness are crucial. The promising results suggest that incorporating uncertainty-aware penalties in model-based frameworks could push the boundaries of what is achievable with offline datasets.

The paper opens avenues for future research, such as enhancing uncertainty estimation techniques and integrating model-free regularization ideas to further stabilize learning in narrow data distributions. The empirical observation that model-based approaches outperform model-free counterparts in offline settings is thought-provoking, warranting deeper investigation into why models facilitate better generalization in batch settings.

In conclusion, the MOPO framework represents a significant advancement in offline RL, providing a more practical and theoretically grounded approach for policy optimization from static datasets. Its success lays a foundation for future innovations in leveraging model-based approaches in various real-world applications.