Agnostic System Identification for Model-Based Reinforcement Learning (1203.1007v2)

Abstract: A fundamental problem in control is to learn a model of a system from observations that is useful for controller synthesis. To provide good performance guarantees, existing methods must assume that the real system is in the class of models considered during learning. We present an iterative method with strong guarantees even in the agnostic case where the system is not in the class. In particular, we show that any no-regret online learning algorithm can be used to obtain a near-optimal policy, provided some model achieves low training error and access to a good exploration distribution. Our approach applies to both discrete and continuous domains. We demonstrate its efficacy and scalability on a challenging helicopter domain from the literature.

• The paper proposes an iterative no-regret online learning method that robustly identifies system models to achieve near-optimal policies in agnostic settings.
• The methodology combines a batch approach with an iterative strategy, addressing train-test mismatches by mixing exploration with policy-driven data collection.
• Experimental results in simulated helicopter maneuvers demonstrate superior performance under delays and noise, emphasizing its potential in robotics and control systems.

Agnostic System Identification for Model-Based Reinforcement Learning

The paper "Agnostic System Identification for Model-Based Reinforcement Learning" by Ross and Bagnell introduces a novel iterative methodology for tackling the challenges of system identification in Model-Based Reinforcement Learning (MBRL). The methodology is designed to provide robust performance guarantees even in agnostic conditions, where the true system model may not be part of the model class considered. This contrasts with many existing methods, which rely heavily on the assumption that the true system lies within the selected class of models.

Core Contributions

The paper's primary contribution is a method that uses no-regret online learning algorithms to develop a nearly optimal policy. This approach is effective as long as there exists a model within the chosen class that achieves low training error and that there is access to a good exploration distribution. The authors propose two main strategies: a simple batch method and a more sophisticated online learning-based iterative method, both of which can be applied to discrete and continuous domains.

Batch Method

The Batch method utilizes a state-action exploration distribution to gather data samples, which are then used to identify the best model within the specified class according to predictive error metrics like $L_1$ loss, KL divergence, or a classification loss. Following model identification, optimal control (OC) procedures are used to deduce a policy under the learned model. However, the batch method's performance is often limited by a train-test mismatch, particularly due to differences between data sampled during exploration and the actual distribution encountered during policy execution.

Iterative No-Regret Approach

The paper introduces a more potent iterative method inspired by the DAgger algorithm in imitation learning. This method systematically alternates between data collection and model updating, using a distribution that mixes exploration policy data with data from executing the current policy. The approach ensures that performance bounds do not scale with the MDP size when provided with an effective exploration distribution. It also leverages reduction-style guarantees, ensuring that if no models with acceptable error exist, a better model class is necessary rather than a fault in the procedural method.

Evaluation and Analysis

The paper presents robust theoretical analysis, demonstrating guarantees that mirror the strongest model-free RL guarantees. Furthermore, it explores how exploration distributions and model classes implicitly influence performance, providing concrete lemmas and theorems that articulate these relationships.

In experimental studies focusing on a simulated helicopter maneuvers domain—such as hovering and nose-in funnel maneuvers—the iterative method shows superior performance across varying delays and noise conditions compared to traditional methods. Notably, it helps discover policies that perform better than both the expert demonstrations and resulting policies from batch methodologies.

Implications and Future Directions

This work significantly impacts practical reinforcement learning applications in robotics and control systems, where guaranteeing high performance from imperfect models is crucial. The proposed approach mitigates model class limitations by equating the task of policy improvement with error minimization in a no-regret online learning framework.

The methodological advancements make it feasible to apply MBRL in more complex systems while maintaining theoretical guarantees. Future research could explore extensions to more diverse model classes and real-world tasks, along with optimizing computational efficiency in iterative policy improvements. Additionally, further investigating the interactions between model exploration and exploitation in dynamic environments would be beneficial, meriting potential development of adaptive exploration strategies.

In conclusion, this paper lays the groundwork for exploring more versatile and assured methods in model-based reinforcement learning, especially within agnostic and unpredictable environments, thereby broadening the horizons of practical implementation and theoretical understanding in control systems.