Combining Model-Based and Model-Free Updates for Trajectory-Centric Reinforcement Learning (1703.03078v3)

Abstract: Reinforcement learning (RL) algorithms for real-world robotic applications need a data-efficient learning process and the ability to handle complex, unknown dynamical systems. These requirements are handled well by model-based and model-free RL approaches, respectively. In this work, we aim to combine the advantages of these two types of methods in a principled manner. By focusing on time-varying linear-Gaussian policies, we enable a model-based algorithm based on the linear quadratic regulator (LQR) that can be integrated into the model-free framework of path integral policy improvement (PI2). We can further combine our method with guided policy search (GPS) to train arbitrary parameterized policies such as deep neural networks. Our simulation and real-world experiments demonstrate that this method can solve challenging manipulation tasks with comparable or better performance than model-free methods while maintaining the sample efficiency of model-based methods. A video presenting our results is available at https://sites.google.com/site/icml17pilqr

• The paper introduces the PILQR algorithm, which integrates model-based LQR-FLM with model-free PI² for trajectory-centric policy optimization.
• It employs a two-stage update mechanism where an initial efficient model-based phase is refined by a model-free phase to correct modeling errors.
• Using Guided Policy Search, PILQR effectively trains deep neural network policies for complex robotic tasks with improved sample efficiency.

Analysis of the Integration of Model-Based and Model-Free Reinforcement Learning for Trajectory Optimization

The paper "Combining Model-Based and Model-Free Updates for Trajectory-Centric Reinforcement Learning" presents a hybrid approach designed to leverage the strengths of both model-based and model-free reinforcement learning strategies. This methodology is particularly aimed at optimizing trajectory-centric policies, suitable for complex robotic applications that require not only efficient data usage but also the capability to deal with complex unmodeled dynamics.

The core idea of this research is the integration of model-based Linear Quadratic Regulator with Fitted Linear Models (LQR-FLM) and model-free Path Integral Policy Improvement (PI$^2$) methodologies to create a robust learning algorithm, coined as PILQR. This approach addresses the typical trade-offs in reinforcement learning where model-based methods, while sample efficient, suffer from bias due to approximation errors in modeling the dynamics, whereas model-free methods, though unbiased, require a large number of samples to learn effective policies.

Key Contributions

1. Hybrid Learning Algorithm PILQR: By proposing PILQR, the authors have combined LQR-FLM's sample efficiency with the flexibility of PI$^2$ to correct for model biases, particularly useful in scenarios where the dynamics are either too complex or discontinuous.
2. Two-Stage PI$^2$ Update Mechanism: The approach delineates the policy update into a model-based stage leveraging LQR-FLM for efficient initial optimization, followed by a model-free stage that handles dynamics that the model cannot capture accurately.
3. Training Parametric Policies: Using the Guided Policy Search (GPS) framework, PILQR facilitates the training of deep neural network policies by leveraging optimized trajectory-centric controllers, providing effective samples for general policy optimization.

Experimental Evaluation and Results

The results from both simulation and real-world experiments demonstrate that PILQR is capable of achieving performance comparable or superior to traditional model-free methods, with substantially increased sample efficiency. For instance, in the simulation environment, PILQR effectively manages tasks such as the door-opening problem, which poses challenges due to contact dynamics not easily captured by models. Moreover, in the context of learning on a real PR2 robot, tasks like hockey playing and power plug insertion were completed with high precision and robustness, showcasing the method's practical applicability.

Implications and Future Directions

The ability of PILQR to handle complex, real-world robotic tasks with fewer samples holds significant implications for deploying RL in industrial applications where data collection is expensive or time-consuming. Moreover, the successful integration with GPS to train deep neural networks indicates possibilities for extending this learning strategy to other domains requiring high-dimensional policy representations.

Future work can build upon this methodology by exploring more sophisticated model parametrizations or adaptive mechanisms for dynamically shifting the balance between model-based and model-free updates depending on the complexity of the tasks. Furthermore, expanding the applicability of PILQR to environments with irregular initial conditions or implementing it within more general purpose RL frameworks could enhance the versatility of this approach.

In conclusion, this paper not only provides a nuanced pathway for advancing trajectory-centric RL through hybrid methods but also opens up avenues for broader applications in robotics and beyond, presenting a compelling case for the utility of integrated learning paradigms in reinforcement learning.