Data-Efficient Reinforcement Learning with Probabilistic Model Predictive Control (1706.06491v2)

Abstract: Trial-and-error based reinforcement learning (RL) has seen rapid advancements in recent times, especially with the advent of deep neural networks. However, the majority of autonomous RL algorithms require a large number of interactions with the environment. A large number of interactions may be impractical in many real-world applications, such as robotics, and many practical systems have to obey limitations in the form of state space or control constraints. To reduce the number of system interactions while simultaneously handling constraints, we propose a model-based RL framework based on probabilistic Model Predictive Control (MPC). In particular, we propose to learn a probabilistic transition model using Gaussian Processes (GPs) to incorporate model uncertainty into long-term predictions, thereby, reducing the impact of model errors. We then use MPC to find a control sequence that minimises the expected long-term cost. We provide theoretical guarantees for first-order optimality in the GP-based transition models with deterministic approximate inference for long-term planning. We demonstrate that our approach does not only achieve state-of-the-art data efficiency, but also is a principled way for RL in constrained environments.

• The paper introduces a GP-based probabilistic MPC approach that significantly reduces data requirements for RL in robotics.
• It leverages Gaussian Processes to model system dynamics and integrate uncertainties into control optimization under constraints.
• Empirical results show the framework outperforms methods like PILCO, achieving faster learning with fewer interactions in constrained environments.

Data-Efficient Reinforcement Learning with Probabilistic Model Predictive Control

The paper presented in the paper focuses on addressing the critical challenge of data inefficiency in reinforcement learning (RL) for real-world applications, highlighting the impractical requirement for large interaction datasets in domains such as robotics, where constraints such as state and control boundaries are prevalent. To tackle this issue, the researchers have formulated a novel model-based RL framework built on probabilistic Model Predictive Control (MPC) utilizing Gaussian Processes (GPs) for learning the probabilistic transition models.

Methodology

The researchers propose an RL framework that leverages probabilistic MPC and GPs to accomplish data efficiency and constraint handling. This approach involves:

1. GP-based Transition Models: By employing Gaussian Processes to model system dynamics, the approach integrates model uncertainty into long-term predictions. This probabilistic modeling mitigates the impact of inaccuracies in the learned dynamics model.
2. MPC for Control Optimization: The framework utilizes MPC for deriving a control sequence that minimizes the expected long-term cost under the constraints. The optimization incorporates model uncertainties, allowing for principled handling of constraints.
3. Theoretical Guarantees: The paper provides theoretical guarantees for first-order optimality using deterministic approximate inference for long-term planning within the GP-based models. This reformulation allows for the use of Pontryagin's Maximum Principle (PMP) to derive control laws in constrained environments.

Empirical Evaluation

The paper empirically evaluates the proposed approach through benchmark RL tasks, such as the cart-pole and double-pendulum swing-up, comparing its performance against PILCO and a zero-variance zero-uncertainty deterministic model-based MPC. The results demonstrate that the proposed GP-MPC achieves superior data efficiency, requiring fewer interactions to reach similar or better performance levels compared to existing state-of-the-art methods.

Key observations from the experiments include:

• Data Efficiency: The proposed GP-MPC method outperformed PILCO in terms of learning speed, achieving high success rates with less real-world data consumed.
• Handling Constraints: The ability to incorporate state and control constraints naturally within the optimization process was demonstrated effectively in constrained scenarios, where GP-MPC managed to accomplish tasks with fewer violations compared to other methods.

Implications and Future Directions

The implications of integrating probabilistic modeling with MPC are significant, particularly in systems where data acquisition is costly or constrained. The framework provides a pathway to enhancing the efficiency of learning control policies in robotics and other real-world applications. Theoretically, it extends the applicability of optimal control principles to environments characterized by uncertainty and hard constraints.

Future investigations could explore extending this approach to a wider spectrum of RL challenges, including those dominated by high-dimensional state spaces or environments with contrasting dynamics. Further, the exploration of alternative probabilistic models and leveraging recent advancements in GPU computing for scalability of GP computations could present fruitful avenues for research.