Model-Based Reinforcement Learning via Meta-Policy Optimization (1809.05214v1)

Abstract: Model-based reinforcement learning approaches carry the promise of being data efficient. However, due to challenges in learning dynamics models that sufficiently match the real-world dynamics, they struggle to achieve the same asymptotic performance as model-free methods. We propose Model-Based Meta-Policy-Optimization (MB-MPO), an approach that foregoes the strong reliance on accurate learned dynamics models. Using an ensemble of learned dynamic models, MB-MPO meta-learns a policy that can quickly adapt to any model in the ensemble with one policy gradient step. This steers the meta-policy towards internalizing consistent dynamics predictions among the ensemble while shifting the burden of behaving optimally w.r.t. the model discrepancies towards the adaptation step. Our experiments show that MB-MPO is more robust to model imperfections than previous model-based approaches. Finally, we demonstrate that our approach is able to match the asymptotic performance of model-free methods while requiring significantly less experience.

• The paper introduces MB-MPO, a framework that integrates meta-learning with model-based reinforcement learning to rapidly adapt policies through a single policy gradient update.
• It leverages an ensemble of learned dynamics models, significantly improving sample efficiency and robustness compared to traditional methods.
• The approach effectively mitigates model bias, achieving optimal performance in complex, real-world control tasks like quadrupedal locomotion.

Model-Based Reinforcement Learning via Meta-Policy Optimization

The paper "Model-Based Reinforcement Learning via Meta-Policy Optimization" proposes an innovative approach to address the inherently challenging aspects of model-based reinforcement learning (MBRL). Given that most recent successes in reinforcement learning (RL) have been achieved through model-free (MF) methods, there is a tangible need for efficient MBRL techniques, especially in scenarios where obtaining experience is computationally expensive, such as in robotic control tasks.

The authors introduce Model-Based Meta-Policy-Optimization (MB-MPO), a method that augments the traditional model-based approach by integrating meta-learning principles. The key insight is to shift away from relying on a single, highly accurate learned dynamics model. Instead, MB-MPO employs an ensemble of learned dynamics models and reframes policy optimization as a meta-learning problem. Through this, MB-MPO meta-learns a policy capable of quickly adapting to variations within the ensemble of models via a single policy gradient step. This adjustment strategy effectively cuts down the reliance on exact dynamics predictions, thereby mitigating model-bias, which is a known problem in the field when the model's imperfections lead to suboptimal behavior.

Methodology and Experimental Insights

MB-MPO is constructed to meta-learn a policy that is robust against the discrepancies among an ensemble of models. The process implicitly divides the policy learning into pre-update and post-update phases employing the Model Agnostic Meta-Learning (MAML) framework. The pre-update policy is robustly optimized over the ensemble, while the post-update phase fine-tunes the policy towards specific dynamics experienced.

The experimental results reveal several significant findings:

1. Performance & Efficiency: The paper demonstrates that MB-MPO matches the asymptotic performance of state-of-the-art model-free methods with significantly fewer samples, enhancing sample efficiency.
2. Robustness: MB-MPO is shown to outperform prior model-based approaches, especially under model inaccuracies and longer horizon tasks. For instance, the method achieves optimal policies in complex quadrupedal locomotion within hours using real-world data.
3. Model-Bias Alleviation: The empirical analysis showcases MB-MPO's ability to learn effectively from biased and noisy models by fine-tuning the policy through meta-learned adaptations.

The experiments conducted in Mujoco environments, alongside benchmark comparisons against methods such as Deep Deterministic Policy Gradient (DDPG) and Model-Ensemble TRPO (ME-TRPO), clearly illustrate the robustness and efficiency derived from the MB-MPO approach.

Implications and Future Work

The integration of meta-learning with model-based reinforcement learning as seen in MB-MPO potentially sets a new direction for handling real-world applications where data efficiency is paramount, such as robotics. The use of ensemble models promotes diversity in policy learning and data collection, ensuring the rapid updating of dynamics models and steering clear of model overfitting.

Looking forward, there are several pathways for extending this research:

• Bayesian Models: Leveraging Bayesian neural networks to encapsulate the distribution of dynamics could further refine the model uncertainty representation, offering a more principled approach to handling variability in predictions.
• Real-World Applications: The real-world application of MB-MPO, particularly in high-stakes robotics and automation, could revolutionize how RL methods are deployed in practical settings, providing a mechanism for balancing between robust planning and adaptable learning.

In conclusion, the MB-MPO framework represents a substantial step forward in model-based reinforcement learning. Its robust sample efficiency and adaptability mark it as a crucial development, with promising future applications in AI and robotics.