Neural Network Dynamics for Model-Based Deep Reinforcement Learning with Model-Free Fine-Tuning (1708.02596v2)

Abstract: Model-free deep reinforcement learning algorithms have been shown to be capable of learning a wide range of robotic skills, but typically require a very large number of samples to achieve good performance. Model-based algorithms, in principle, can provide for much more efficient learning, but have proven difficult to extend to expressive, high-capacity models such as deep neural networks. In this work, we demonstrate that medium-sized neural network models can in fact be combined with model predictive control (MPC) to achieve excellent sample complexity in a model-based reinforcement learning algorithm, producing stable and plausible gaits to accomplish various complex locomotion tasks. We also propose using deep neural network dynamics models to initialize a model-free learner, in order to combine the sample efficiency of model-based approaches with the high task-specific performance of model-free methods. We empirically demonstrate on MuJoCo locomotion tasks that our pure model-based approach trained on just random action data can follow arbitrary trajectories with excellent sample efficiency, and that our hybrid algorithm can accelerate model-free learning on high-speed benchmark tasks, achieving sample efficiency gains of 3-5x on swimmer, cheetah, hopper, and ant agents. Videos can be found at https://sites.google.com/view/mbmf

• The paper presents a hybrid approach that combines model-based RL’s sample efficiency with model-free fine-tuning to achieve robust high-performance control.
• It employs multi-layer neural network dynamics with iterative data aggregation to mitigate state-action distribution mismatch and enhance model accuracy.
• Empirical results on MuJoCo tasks show 3–5× sample efficiency gains, underscoring its potential for cost-effective real-world robotic applications.

Neural Network Dynamics for Model-Based Deep Reinforcement Learning with Model-Free Fine-Tuning

The paper "Neural Network Dynamics for Model-Based Deep Reinforcement Learning with Model-Free Fine-Tuning" by Nagabandi et al. presents a method for integrating model-based and model-free deep reinforcement learning (DRL) to enhance sample efficiency while achieving high task-specific performance. This synthesis addresses the significant trade-off between sample efficiency and asymptotic performance encountered in standard DRL algorithms.

Key Contributions

1. Neural Network Dynamics in Model-Based RL: The authors demonstrate that multi-layer neural networks can be effectively integrated with model-based RL algorithms. This work introduces a medium-sized neural network model that, combined with model predictive control (MPC) using a random-sampling shooting method, significantly improves sample complexity. The trained dynamics model can control agents like the swimmer, half-cheetah, hopper, and ant on MuJoCo benchmark tasks with high efficiency.
2. Data Aggregation for Improved Model Training: A core component of the proposed method is the use of an iterative data aggregation procedure to mitigate issues arising from the state-action distribution mismatch. This approach involves alternating between training the model with collected data and gathering new on-policy data using the current model, enhancing the model's robustness and predictive accuracy.
3. Model-Free Fine-Tuning: To bridge the gap in performance between model-based and model-free methods, the paper proposes initializing a model-free learner with a policy derived from the model-based controller. This hybrid approach, referred to as model-based model-free (Mb-Mf), leverages the sample efficiency of model-based methods while attaining the high asymptotic performance characteristic of model-free methods. Empirical results show that this combined approach achieves sample efficiency gains of $3-5\times$ on various MuJoCo locomotion tasks.

Empirical Results

The authors carried out extensive experiments on several MuJoCo simulated environments. The model-based approach demonstrated capable trajectory following and could quickly learn effective locomotion gaits with minimal data. For example, in the trajectory following task, even after training purely on random actions, the learned models were sufficiently general to follow arbitrary paths at test time. This capability suggests strong generalization properties of the dynamics model.

Notably, the Mb-Mf approach showed significant improvements over pure model-free learning (TRPO). On benchmark tasks, the hybrid method markedly reduced the number of required samples to achieve near-optimal performance. For instance, in the swimmer environment, the model-based learner reached a competent gait within $20\times$ fewer samples, and the hybrid method achieved the same high performance as TRPO in only $3\times$ fewer training steps.

Implications and Future Directions

The integration of model-based and model-free methodologies addresses critical challenges in reinforcement learning, particularly regarding sample efficiency and the ability to achieve high-performance levels. This hybrid approach can be transformative for practical applications involving real-world robotic systems where data collection is expensive and time-consuming.

Future developments could involve a tighter integration of model-based components with different model-free algorithms such as Q-learning and actor-critic methods. Exploring more sophisticated control techniques beyond random-sampling MPC could further enhance performance and robustness. Moreover, real-world deployments pose intriguing avenues for research, leveraging the improved sample efficiency to train robots directly in physical environments effectively.

The findings presented in this paper set a strong foundation for advancing reinforcement learning's practical applicability, demonstrating that combining the strengths of model-based and model-free methods can yield substantial benefits across a range of tasks and environments.