COMBO: Conservative Offline Model-Based Policy Optimization (2102.08363v2)

Abstract: Model-based algorithms, which learn a dynamics model from logged experience and perform some sort of pessimistic planning under the learned model, have emerged as a promising paradigm for offline reinforcement learning (offline RL). However, practical variants of such model-based algorithms rely on explicit uncertainty quantification for incorporating pessimism. Uncertainty estimation with complex models, such as deep neural networks, can be difficult and unreliable. We overcome this limitation by developing a new model-based offline RL algorithm, COMBO, that regularizes the value function on out-of-support state-action tuples generated via rollouts under the learned model. This results in a conservative estimate of the value function for out-of-support state-action tuples, without requiring explicit uncertainty estimation. We theoretically show that our method optimizes a lower bound on the true policy value, that this bound is tighter than that of prior methods, and our approach satisfies a policy improvement guarantee in the offline setting. Through experiments, we find that COMBO consistently performs as well or better as compared to prior offline model-free and model-based methods on widely studied offline RL benchmarks, including image-based tasks.

• The paper introduces COMBO, a conservative model-based offline RL algorithm that overcomes overestimation bias by regulating Q-value estimates on out-of-support data.
• COMBO leverages a penalty mechanism on state-action pairs and bypasses explicit uncertainty quantification, offering theoretical guarantees and safe policy improvement.
• Empirical evaluations on benchmarks, including image-based tasks and standard D4RL, demonstrate COMBO’s robust performance and superior generalization compared to prior methods.

Insightful Overview of COMBO: Conservative Offline Model-Based Policy Optimization

The paper under review introduces COMBO, a novel algorithm designed for offline reinforcement learning (RL). The offline RL paradigm focuses on training policies using pre-existing, static datasets, rather than relying on new interactions with the environment. This setting is especially relevant for applications where data collection is expensive, risky, or impractical, such as healthcare, robotics, and autonomous driving.

Key Contributions and Methodology

COMBO distinguishes itself by integrating model-based approaches with a conservative value function estimation strategy. Unlike prior offline model-based RL algorithms, such as MOPO and MOReL, COMBO bypasses the need for explicit uncertainty quantification of the learned dynamics model. The authors argue that uncertainty estimation is challenging and often unreliable, especially when dealing with complex datasets and deep neural network models.

The framework presented in the paper revolves around two primary phases: conservative policy evaluation and policy improvement. During the evaluation phase, COMBO trains the Q-function using both the offline dataset and additional data generated through model rollouts, but with an important conservative twist. By regularizing the Q-value estimates for out-of-support state-action pairs—those not well-represented in the offline dataset—COMBO is able to provide a conservative approximation of the value function. This is crucial for mitigating the overestimation bias that typically hampers offline RL algorithms.

Notably, COMBO employs a penalty mechanism that leverages a sampling distribution $\rho(s, a)$ to prioritize more reliable state-action tuples while penalizing the rest. This is formalized by an expectation under $\rho(s, a)$ and offers a novel approach to achieving a less conservative value function compared to model-free methods such as Conservative Q-Learning (CQL).

Theoretical Guarantees

The paper delivers significant theoretical contributions, showing that COMBO optimizes a lower bound on the expected return of policies. The Q-function learned using COMBO is demonstrated to be a conservative estimator of the true Q-function under realistic assumptions about sampling errors and model bias. Moreover, the authors provide rigorous analysis illustrating conditions under which COMBO's lower bound is tighter than that of model-free counterparts like CQL.

Additionally, the authors present a safe policy improvement guarantee. This ensures that the learned policy, $\hat{\pi}$, improves over the behavior policy employed in the logged data, thereby making COMBO particularly appealing for deployment in sensitive applications where safety or reliability is critical.

Empirical Evaluation

The experimental results presented in the manuscript highlight COMBO’s superior performance over several baseline methods on a variety of tasks. These include tasks requiring generalization to previously unseen behaviors, image-based tasks, and standard benchmarks. In environments demanding significant generalization, COMBO outperforms approaches like MOPO and CQL, demonstrating its robustness and adaptability.

Furthermore, in image-based settings, COMBO is successfully extended to operate within a latent space, achieving high success rates in challenging tasks. On the standard D4RL benchmark, COMBO consistently outperforms or matches both model-based and model-free predecessors, underscoring its versatility across various dataset types and complexities.

Practical and Theoretical Implications

The practical implications of COMBO are profound, especially in domains where safety and data efficiency are paramount. By eschewing the need for state-action visitation in online settings and avoiding pitfalls associated with uncertainty estimation, COMBO aligns with the objectives of deploying RL technologies in the real world. Theoretically, it propounds a shift towards emphasizing model-based rollouts combined with conservative Q-learning, challenging the prevailing reliance on behavior cloning or trust region methods in offline settings.

Future Directions

Given its robust performance and theoretical underpinnings, future research could explore further enhancements of COMBO, such as dynamically adjusting the conservatism based on model confidence or integrating richer representation learning techniques. Additionally, exploring its application in more diverse and larger-scale domains could provide insights into its true capabilities and limitations.

In conclusion, the paper provides a compelling advancement in offline RL through COMBO, balancing model-based optimism with cautious value estimation and offering a promising avenue for safer and more effective policy optimization under offline circumstances.