EPOpt: Learning Robust Neural Network Policies Using Model Ensembles (1610.01283v4)

Abstract: Sample complexity and safety are major challenges when learning policies with reinforcement learning for real-world tasks, especially when the policies are represented using rich function approximators like deep neural networks. Model-based methods where the real-world target domain is approximated using a simulated source domain provide an avenue to tackle the above challenges by augmenting real data with simulated data. However, discrepancies between the simulated source domain and the target domain pose a challenge for simulated training. We introduce the EPOpt algorithm, which uses an ensemble of simulated source domains and a form of adversarial training to learn policies that are robust and generalize to a broad range of possible target domains, including unmodeled effects. Further, the probability distribution over source domains in the ensemble can be adapted using data from target domain and approximate Bayesian methods, to progressively make it a better approximation. Thus, learning on a model ensemble, along with source domain adaptation, provides the benefit of both robustness and learning/adaptation.

• The paper introduces the EPOpt-ε algorithm that trains on an ensemble of simulated environments to achieve robust neural network policies.
• It uses adversarial training to focus on low-performing model instances, enhancing stability against simulation-to-real discrepancies.
• Experimental results on robotic tasks like hopper and half-cheetah demonstrate improved policy robustness and effective source domain adaptation.

An Insightful Overview of "EPOpt: Learning Robust Neural Network Policies Using Model Ensembles"

The paper "EPOpt: Learning Robust Neural Network Policies Using Model Ensembles" addresses critical challenges in reinforcement learning (RL), particularly when applied to real-world tasks using deep neural networks (DNNs) as function approximators. The authors propose EPOpt, an algorithm designed to learn robust policies that generalize well across varying conditions by employing model-based RL with model ensembles.

Main Contributions and Methodology

The core contribution of this work is the Ensemble Policy Optimization (EPOpt$-\epsilon$) algorithm. This algorithm is a form of model-based RL that constructs an ensemble of simulated environments representing a distribution over potential real-world conditions. The ensemble approach aims to augment the learning process with a diverse array of scenarios, helping mitigate the systematic discrepancies often present between simulation and real-world applications.

EPOpt combines two pivotal ideas:

1. Adversarial Training on Ensembles: By training on an ensemble of models sampled from a source distribution, EPOpt seeks robust policies that cope with parametric model errors and potentially unmodeled effects. The adversarial nature means the training focuses on model instances where the policy performs poorly, encouraging robustness.
2. Source Domain Adaptation: The source domain ensemble is refined progressively using data from the target domain combined with approximate Bayesian methods. This adaptation enhances the source distribution's fit to the target domain.

Experimental Validation and Algorithmic Strength

The paper evaluates EPOpt on two complex simulated robotic tasks – the hopper and half-cheetah – within the MuJoCo physics simulator. Key findings from these experiments include:

• Policy Robustness: EPOpt-trained policies exhibit significantly more robustness to variations in task parameters than traditional TRPO-trained policies. Specifically, policies trained on ensemble distributions demonstrate stable performance across different model discrepancies, crucial for successful transfer from simulations to physical systems.
• Adversarial Training Impact: The adversarial aspect of EPOpt (EPOpt$-\epsilon$ with $\epsilon=0.1$) enhances robustness, decreasing variability in performance without substantial degradation.
• Effective Model Adaptation: Effective source domain adaptation is demonstrated through rapid improvements in policy performance with minimal data from the target domain.

Implications and Future Directions

Practically, EPOpt provides a structured approach to achieving robustness in RL policies, which is particularly valuable for real-world deployments where discrepancies between training simulations and actual environments are prevalent. Theoretically, this work bridges robust control principles with model-based Bayesian RL, positioning EPOpt as a promising technique for tasks involving dynamic and uncertain environments.

The future developments could explore extending EPOpt to more complex models and broader parameter spaces, potentially integrating adaptive sampling techniques to manage computational demands. Another avenue is learning neural network models that entirely replace physics-based simulators, leveraging EPOpt's robustness to improve generalization and reliability in unpredictable real-world scenarios.

The EPOpt framework represents a substantial step forward in the quest for robust, adaptable RL policies and holds promise for advancing AI applications in dynamic and uncertain domains.