Sample-Efficient Reinforcement Learning with Stochastic Ensemble Value Expansion (1807.01675v2)

Abstract: Integrating model-free and model-based approaches in reinforcement learning has the potential to achieve the high performance of model-free algorithms with low sample complexity. However, this is difficult because an imperfect dynamics model can degrade the performance of the learning algorithm, and in sufficiently complex environments, the dynamics model will almost always be imperfect. As a result, a key challenge is to combine model-based approaches with model-free learning in such a way that errors in the model do not degrade performance. We propose stochastic ensemble value expansion (STEVE), a novel model-based technique that addresses this issue. By dynamically interpolating between model rollouts of various horizon lengths for each individual example, STEVE ensures that the model is only utilized when doing so does not introduce significant errors. Our approach outperforms model-free baselines on challenging continuous control benchmarks with an order-of-magnitude increase in sample efficiency, and in contrast to previous model-based approaches, performance does not degrade in complex environments.

• The paper presents STEVE, which integrates model-based and model-free techniques to markedly reduce the samples needed for effective RL performance.
• It employs a dynamic interpolation technique with inverse-variance weighting to adjust rollout horizons based on prediction uncertainty.
• Empirical results demonstrate an order-of-magnitude improvement in sample efficiency on challenging continuous control tasks.

Insights into Sample-Efficient Reinforcement Learning with Stochastic Ensemble Value Expansion

The paper discusses the integration of model-free and model-based techniques in reinforcement learning (RL) to enhance performance while maintaining low sample complexity. The proposed method, Stochastic Ensemble Value Expansion (STEVE), introduces a novel approach to mitigate the challenges posed by imperfect dynamics models in environments with complex dynamics. This innovative method aims to optimally balance model-based and model-free learning paradigms.

Overview

The authors recognize the sample efficiency bottleneck in deep model-free RL, which has achieved impressive results in domains such as video games and strategic board games but requires a prohibitively large number of samples for most practical applications. On the other hand, model-based approaches, which attempt to learn environment dynamics to improve sample efficiency, often struggle with model inaccuracies that degrade overall performance.

STEVE addresses these challenges by using a dynamic interpolation technique. This method allows model rollouts of various horizon lengths based on uncertainty estimates, ensuring the model is utilized primarily when it provides more precise predictions. This adaptive mechanism prevents performance degradation, enhancing sample efficiency without the need for excessive model fidelity—a common issue in complex environments.

Methodology

In detail, STEVE leverages both an ensembled model and Q-function to estimate the uncertainty in the model predictions. By dynamically adjusting the rollout horizon based on the calculated uncertainty, STEVE can make informed decisions on the optimal balance between rolling out the model and relying on model-free learning estimates. This entails:

• Computing rollouts of different lengths and assessing the variance of these estimates.
• Using inverse-variance weighting to interpolate among candidate rollouts, ensuring lower-variance predictions are favored.

The ensemble approach to modeling and value function estimation is critical to this methodology. It provides a principled way to assess the uncertainty and adapt the reliance on predictions from the environment dynamics model, essentially performing integration over multiple hypotheses to absorb the aspect of prediction errors.

Numerical Results

The empirical results highlight STEVE's efficacy, demonstrating significant improvements over baseline model-free approaches on challenging continuous control tasks. These improvements manifest as an order-of-magnitude reduction in sample requirements while maintaining robust performance across complex tasks where previous model-based approaches typically degrade.

Implications and Future Directions

STEVE exemplifies a stronger integration of ensemble methods into model-based RL, effectively utilizing the richer information provided by uncertainty estimates. This stands as a substantial progression towards practical RL applications, especially in real-world scenarios where sample collection is costly.

The paper suggests numerous avenues for future research:

• Exploring more advanced modeling techniques to refine uncertainty estimation further.
• Investigating the dynamic interplay between Q-function learning and model usage in more diverse environments.
• Scaling this approach for broader applications in robotics and other fields where efficient learning is paramount.

Conclusion

STEVE presents a compelling approach to reconcile the trade-offs inherent in model-based RL, offering a framework that ensures sample efficiency without compromising the robustness of the learning process. This approach is poised to contribute significantly to the field of reinforcement learning, laying the foundation for subsequent innovations in efficiency-oriented learning methods.