Model-Ensemble Trust-Region Policy Optimization

Abstract: Model-free reinforcement learning (RL) methods are succeeding in a growing number of tasks, aided by recent advances in deep learning. However, they tend to suffer from high sample complexity, which hinders their use in real-world domains. Alternatively, model-based reinforcement learning promises to reduce sample complexity, but tends to require careful tuning and to date have succeeded mainly in restrictive domains where simple models are sufficient for learning. In this paper, we analyze the behavior of vanilla model-based reinforcement learning methods when deep neural networks are used to learn both the model and the policy, and show that the learned policy tends to exploit regions where insufficient data is available for the model to be learned, causing instability in training. To overcome this issue, we propose to use an ensemble of models to maintain the model uncertainty and regularize the learning process. We further show that the use of likelihood ratio derivatives yields much more stable learning than backpropagation through time. Altogether, our approach Model-Ensemble Trust-Region Policy Optimization (ME-TRPO) significantly reduces the sample complexity compared to model-free deep RL methods on challenging continuous control benchmark tasks.

• The paper introduces Model-Ensemble TRPO, a novel method that integrates ensemble modeling for uncertainty management with Trust Region Policy Optimization for robust policy updates.
• The approach achieves state-of-the-art performance by reducing sample complexity by up to 100-fold on complex continuous control tasks like Ant and Humanoid.
• The methodology mitigates instability in model-based RL and bridges the efficiency gap compared to model-free methods in high-dimensional environments.

Model-Ensemble Trust-Region Policy Optimization: Reducing Sample Complexity in Deep Reinforcement Learning

The paper "Model-Ensemble Trust-Region Policy Optimization" introduces a novel methodology for enhancing the efficiency of model-based reinforcement learning (RL) algorithms applied to continuous control tasks. Reinforcement learning, a subset of machine learning, empowers agents to make sequences of decisions by interacting with their environment. Among the two predominant strategies in this domain, model-free algorithms have traditionally displayed robust adaptability across varied tasks but at the cost of high sample complexity. Model-based methods, promising lower sample complexity by learning a model of the environment, often grapple with instability issues, particularly in real-world applications.

This study proposes an innovative approach that amalgamates the strengths of model-based RL approaches while addressing their inherent drawbacks. The core technique introduced is the Model-Ensemble Trust-Region Policy Optimization (ME-TRPO), which significantly curtails the sample complexity associated with learning high-performance policies. The proposed solution revolves around three pivotal innovations: employing an ensemble of models to manage model uncertainty, utilizing Trust Region Policy Optimization (TRPO) for more stable policy learning, and strategy validation using the ensemble during policy updating.

Key Contributions and Numerical Outcomes

1. Model Ensemble for Uncertainty Management: By leveraging an ensemble of deep neural network models, the method accounts for model uncertainty inherently arising from data scarcity in unexplored state regions. Each model in the ensemble is trained under varying initial conditions and sample sequences, which diversifies the learning signals derived from the aggregate set of models.
2. Trust-Region Policy Optimization: The paper underscores using likelihood ratio methods—specifically TRPO—to stabilize the policy learning process. Unlike backpropagation through time (BPTT), which struggles with exploding or vanishing gradients over extended trajectories, the TRPO facilitates more reliable learning without requiring gradient information from the dynamical models.
3. Superior Sample Efficiency: Empirical evaluations demonstrate that ME-TRPO achieves state-of-the-art performance, comparable to advanced model-free algorithms, with significantly reduced sample requirements—up to a 100-fold decrease. This efficiency was empirically validated across various continuous control benchmarks, including complex tasks involving high-dimensional state spaces like Ant and Humanoid environments.

Theoretical and Practical Implications

The introduction of the ensemble-based approach presents a robust strategy against the usual overfitting seen in conventional vanilla model-based RL methodologies. By representing the dynamic uncertainty directly within the algorithmic framework, ME-TRPO provides a more nuanced cognitive span when the learned policy interacts within unpredictable regions of the state-action space. Moreover, substituting BPTT with TRPO alleviates the issues associated with algorithmic exploration in high-dimensional domains.

Future research can explore further enhancements by combining the proposed model ensemble methodology with techniques that encourage exploration in underrepresented state-action regions, boosting data diversity and reducing potential policy stagnancies. Moreover, incorporating this approach in real-world systems, like robotics, could potentially revolutionize the deployment of ML models where data acquisition is especially costly or cumbersome.

In conclusion, the ME-TRPO method stands as a significant stride in reinforcing model-based RL paradigms and streamlining their application in dynamic and complex environments. This scholarly work lays down foundational insights that, looking forward, can inspire innovations towards the broader adoption of reinforcement learning in practical, real-world settings.