Learning Continuous Control Policies by Stochastic Value Gradients (1510.09142v1)

Abstract: We present a unified framework for learning continuous control policies using backpropagation. It supports stochastic control by treating stochasticity in the BeLLMan equation as a deterministic function of exogenous noise. The product is a spectrum of general policy gradient algorithms that range from model-free methods with value functions to model-based methods without value functions. We use learned models but only require observations from the environment in- stead of observations from model-predicted trajectories, minimizing the impact of compounded model errors. We apply these algorithms first to a toy stochastic control problem and then to several physics-based control problems in simulation. One of these variants, SVG(1), shows the effectiveness of learning models, value functions, and policies simultaneously in continuous domains.

• The paper introduces stochastic value gradients (SVG) which re-parameterize stochasticity to enable effective backpropagation in complex continuous control tasks.
• It presents a unified framework that combines model-free techniques with model-based approaches, reducing gradient variance and mitigating model errors.
• Experimental results show that SVG(1), particularly with experience replay, outperforms standard actor-critic methods in high-dimensional physics-based simulations.

Continuous Control Policies via Stochastic Value Gradients

The paper by Heess et al. introduces a framework for optimizing continuous control policies using Stochastic Value Gradients (SVG), effectively addressing some limitations of prior value gradient methods. This work extends policy gradient approaches by incorporating stochastic policy optimizations within stochastic environments and integrates elements from model-based and model-free reinforcement learning methods.

Unified Framework and Methodology

The proposed framework unifies the learning of continuous control policies by treating stochasticity in the BeLLMan equation deterministically through re-parameterization, enabling backpropagation in stochastic settings. By integrating both model-free techniques leveraging value functions and model-based approaches without them, the authors introduce a spectrum of general policy gradient algorithms, notably SVG(1). This variant learns models, value functions, and policies simultaneously in continuous domains, minimizing compounded model errors through direct environment interaction.

Theoretical Contributions

1. Stochastic Value Gradients (SVG): SVG methods extend the applicability of value gradient approaches to stochastic policies in stochastic environments using re-parameterization, a technique that models stochasticity as a deterministic function of noise.
2. Combining Methods: By flexibly combining learned dynamics models with value functions, SVG introduces a distinctive blend of model-based and model-free methods, enabling robust policy optimization in complex environments.
3. Regression and Gradient Estimation: SVG delineates how future rewards can be estimated and optimized using learned value functions or Q-functions, enhancing robustness against model inaccuracies and reducing variance in gradient estimations.

Experimental Findings

The authors validate the SVG framework across various control tasks, from simple stochastic problems to complex, high-dimensional physics-based simulations. Key numerical results include:

• Model Robustness: SVG(1), particularly with experience replay (SVG(1)-ER), demonstrated better resilience to model error than purely model-based methods, maintaining robust performance as model capacity decreases.
• Performance: In several physics-based control problems, such as robotic arm manipulation and locomotion tasks, SVG algorithms successfully optimized complex control policies, outperforming model-free approaches like standard actor-critic algorithms.

Implications and Future Directions

The research presents significant implications both theoretically and practically. Theoretically, it offers insights into integrating differentiable models with policy learning, enhancing our understanding of combining model-based and model-free paradigms. Practically, SVG methods pave the way for more efficient and robust learning algorithms in robotics and other domains requiring continuous control.

Future developments could explore the application of more sophisticated generative models for stochastic dynamics, potentially improving performance in highly uncertain environments. Additionally, refining the computation of SVG algorithm variants and leveraging advances in neural architectures could further enhance their applicability across a spectrum of control tasks.

Conclusion

In summary, the paper presents a comprehensive paper of Stochastic Value Gradient methods, contributing to the field of reinforcement learning by expanding the scope of value gradient algorithms and demonstrating their efficacy in continuous control environments. These contributions lay a foundational step toward more unified and flexible reinforcement learning applications.