Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models (1805.12114v2)

Abstract: Model-based reinforcement learning (RL) algorithms can attain excellent sample efficiency, but often lag behind the best model-free algorithms in terms of asymptotic performance. This is especially true with high-capacity parametric function approximators, such as deep networks. In this paper, we study how to bridge this gap, by employing uncertainty-aware dynamics models. We propose a new algorithm called probabilistic ensembles with trajectory sampling (PETS) that combines uncertainty-aware deep network dynamics models with sampling-based uncertainty propagation. Our comparison to state-of-the-art model-based and model-free deep RL algorithms shows that our approach matches the asymptotic performance of model-free algorithms on several challenging benchmark tasks, while requiring significantly fewer samples (e.g., 8 and 125 times fewer samples than Soft Actor Critic and Proximal Policy Optimization respectively on the half-cheetah task).

• The paper introduces the PETS algorithm that leverages uncertainty-aware probabilistic ensembles and trajectory sampling for robust model-based RL.
• It achieves high sample efficiency, using up to 125 times fewer samples than conventional model-free methods while matching asymptotic performance.
• By isolating epistemic from aleatoric uncertainty, PETS enables targeted exploration, making it promising for practical applications like robotics and autonomous systems.

Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models

This paper presents an enhanced model-based reinforcement learning (MBRL) algorithm designed to effectively utilize uncertainty-aware dynamics models. The authors introduce a new algorithm, Probabilistic Ensembles with Trajectory Sampling (PETS), which integrates high-capacity neural network models with an advanced sampling-based uncertainty propagation approach to achieve competitive performance on reinforcement learning tasks.

The primary motivation for this paper arises from the intrinsic challenges associated with existing reinforcement learning algorithms. Model-free RL algorithms, although effective in some domains, often require infeasibly large amounts of data. Model-based approaches, while offering superior sample efficiency, lag in asymptotic performance. To mitigate these limitations, PETS leverages probabilistic ensembles and trajectory sampling to yield both high sample efficiency and strong asymptotic performance.

Algorithm Design and Implementation

The PETS algorithm is built upon two significant components:

1. Probabilistic Ensembles: The authors employ an ensemble of neural networks, where each model in the ensemble provides probabilistic predictions that encapsulate both aleatoric (inherent in the system) and epistemic (due to data insufficiency) uncertainties. This dual uncertainty modeling enhances the robustness of the prediction model.
2. Trajectory Sampling: Instead of relying on deterministic propagation or simple probabilistic propagation, PETS uses trajectory sampling. This involves creating multiple particles representing potential state trajectories, each particle being propagated using different models from the probabilistic ensemble.

A unique aspect of PETS is the way it isolates epistemic from aleatoric uncertainty, allowing more directed exploration. Alongside the ensemble, Modified Cross-Entropy Method (CEM) is utilized as the optimization technique in Model Predictive Control (MPC). This choice ensures effective and efficient sampling from the distribution of actions.

Comparative Performance Analysis

Empirical evaluation on a diverse set of simulated continuous control tasks demonstrates that PETS achieves notable improvements over state-of-the-art model-free and model-based algorithms. Key findings include:

• On tasks like half-cheetah, PETS requires significantly fewer samples (e.g., 8 times fewer than Soft Actor Critic and 125 times fewer than Proximal Policy Optimization) to reach competitive performance levels.
• PETS rivals the asymptotic performance of Proximal Policy Optimization, traditionally seen as requiring extensive data for convergence.
• The consistent performance of PETS across different domains underscores its robustness to varying dynamics complexities and system dimensionalities.

Implications and Future Directions

The implications of this research extend beyond mere performance improvements. The successful integration of uncertainty-aware models into the decision-making process implies broader applicability in real-world scenarios where data collection is expensive or limited. Autonomous driving, robotic manipulation, and interactive agents could benefit from these advancements.

Theoretical Implications:

• Uncertainty Modeling: The distinction between aleatoric and epistemic uncertainty in neural network models opens new avenues for more nuanced exploration strategies in reinforcement learning.
• Model Design: This paper underscores the effectiveness of probabilistic ensembles supplemented with trajectory sampling in achieving sample efficiency without compromising on performance.

Practical Implications:

• Real-world Deployment: Model-based algorithms like PETS, which require fewer interactions with the environment, are more feasible for applications in robotics where real-time adaptation and learning are critical.
• Energy and Cost Efficiency: Reduced sample complexity implies lower computational costs and energy consumption, making this approach suitable for resource-constrained settings.

Conclusion

The findings from this paper establish PETS as a significant step forward in the domain of model-based reinforcement learning. By ensuring competitive performance with fewer samples, PETS promises to bridge the gap between sample efficiency and asymptotic performance. Future research could explore the integration of policy learning to further streamline the decision-making process and enhance the applicability of the algorithm in dynamic and unpredictable environments. Overall, PETS provides a compelling framework for harnessing the power of deep learning within the constraints of reinforcement learning.