SAMBO-RL: Shifts-aware Model-based Offline Reinforcement Learning (2408.12830v2)

Abstract: Model-based offline reinforcement learning trains policies using pre-collected datasets and learned environment models, eliminating the need for direct real-world environment interaction. However, this paradigm is inherently challenged by distribution shift (DS). Existing methods address this issue by leveraging off-policy mechanisms and estimating model uncertainty, but they often result in inconsistent objectives and lack a unified theoretical foundation. This paper offers a comprehensive analysis that disentangles the problem into two fundamental components: model bias and policy shift. Our theoretical and empirical investigations reveal how these factors distort value estimation and restrict policy optimization. To tackle these challenges, we derive a novel Shifts-aware Reward (SAR) through a unified probabilistic inference framework, which modifies the vanilla reward to refine value learning and facilitate policy training. Building on this, we introduce Shifts-aware Model-based Offline Reinforcement Learning (SAMBO-RL), a practical framework that efficiently trains classifiers to approximate SAR for policy optimization. Empirical experiments show that SAR effectively mitigates DS, and SAMBO-RL achieves superior or comparable performance across various benchmarks, underscoring its effectiveness and validating our theoretical analysis.

• The paper introduces a novel shifts-aware reward framework that adjusts for model bias and policy shift in offline reinforcement learning.
• It leverages classifier-based adjustments to refine value estimation and optimize policy learning with greater accuracy.
• Empirical results on D4RL benchmarks show robust performance improvements across diverse datasets and simulation environments.

SAMBO-RL: Shifts-aware Model-based Offline Reinforcement Learning

The paper, "SAMBO-RL: Shifts-aware Model-based Offline Reinforcement Learning," by Luo et al., addresses critical challenges in model-based offline reinforcement learning (RL), particularly focusing on the issues of model bias and policy shift. Offline RL, wherein policies are trained using static datasets without real-world environment interaction, holds significant promise for applications where continuous exploration is infeasible or risky. However, the method faces inherent distribution shift problems, leading to suboptimal policy performance.

Theoretical Foundations

This paper provides a rigorous theoretical analysis that disentangles the distribution shift into two primary components:

1. Model Bias: Represents the inconsistency between learned dynamics and actual environment dynamics, causing inaccuracies in state-transition predictions.
2. Policy Shift: Indicates the deviation between the policy used for data collection (behavior policy) and the policy being optimized.

Previous approaches have largely utilized off-policy methods and heuristic uncertainty estimates to address distribution shift. However, these methods often result in biased training objectives and lack a unified theoretical framework. In contrast, the paper adopts a probabilistic inference approach to derive adjustment terms for model bias and policy shift, which are then incorporated into a novel Shifts-aware Reward (SAR).

Shifts-aware Reward and SAMBO-RL Framework

The Shifts-aware Reward integrates adjustments directly into the reward function, refining value learning and facilitating more accurate policy training. This reward form ensures that the training objective aligns more closely with the true objective of reinforcement learning. Specifically, it penalizes model bias and modifies rewards to counter implicit policy restrictions:

1. Model Bias Adjustment: This adjustment penalizes discrepancies between model-predicted and actual state transitions, thereby improving value estimate accuracy and steering learning towards more reliable policy outcomes.
2. Policy Shift Modification: This modification incentivizes deviations from the behavior policy, thus encouraging optimal policy convergence by promoting exploration beyond the limitations imposed by the behavior policy.

The SAMBO-RL framework operationalizes these theoretical insights through a practical algorithm that seamlessly integrates the SAR into the learning process. Key steps include the training of transition and action classifiers to approximate the shifts-aware reward and using the adjusted rewards for policy optimization.

Empirical Evaluation

The proposed method was empirically evaluated against several benchmarks within the D4RL suite. SAMBO-RL demonstrated superior performance in terms of average normalized scores across various environments and dataset configurations. In particular, it achieved the highest scores on the halfcheetah-random and hopper-random datasets, and exhibited robust performance across medium and medium-replay datasets.

Key Results and Contributions

• Robust Handling of Distribution Shift: The SAR effectively mitigates the negative impacts of both model bias and policy shift, leading to better policy performance and value estimation.
• Classifier-based Reward Adjustment: The introduction of transition and action classifiers provides an efficient mechanism for estimating adjustments empirically, validating the theoretical framework.
• Benchmark Performance: SAMBO-RL's performance on D4RL benchmarks underscores its practical effectiveness, particularly in scenarios with diverse data quality.

Future Directions

The paper opens several avenues for future research:

• Enhancing Classifier Accuracy: Given that the success of SAMBO-RL partially hinges on the accuracy of classifiers, future work could explore advanced methods for classifier training and validation.
• Generalizing to Online Settings: Extending the shifts-aware framework to online RL settings might offer insights into addressing policy shift problems in real-time learning scenarios.
• Incorporating Adaptive Mechanisms: Developing adaptive mechanisms for dynamically tuning hyperparameters, like the model bias coefficient and policy shift coefficient, could further refine the balance between exploration and exploitation.

Overall, this paper presents a well-founded theoretical and empirical approach to improving model-based offline RL by focusing on mitigating distribution shift through a unified framework. The novel SAMBO-RL method, with its robust performance and practical relevance, contributes significantly to the domain, suggesting promising directions for both theoretical advancements and practical applications in AI.