Deployment-Efficient Reinforcement Learning via Model-Based Offline Optimization (2006.03647v2)

Abstract: Most reinforcement learning (RL) algorithms assume online access to the environment, in which one may readily interleave updates to the policy with experience collection using that policy. However, in many real-world applications such as health, education, dialogue agents, and robotics, the cost or potential risk of deploying a new data-collection policy is high, to the point that it can become prohibitive to update the data-collection policy more than a few times during learning. With this view, we propose a novel concept of deployment efficiency, measuring the number of distinct data-collection policies that are used during policy learning. We observe that na\"{i}vely applying existing model-free offline RL algorithms recursively does not lead to a practical deployment-efficient and sample-efficient algorithm. We propose a novel model-based algorithm, Behavior-Regularized Model-ENsemble (BREMEN) that can effectively optimize a policy offline using 10-20 times fewer data than prior works. Furthermore, the recursive application of BREMEN is able to achieve impressive deployment efficiency while maintaining the same or better sample efficiency, learning successful policies from scratch on simulated robotic environments with only 5-10 deployments, compared to typical values of hundreds to millions in standard RL baselines. Codes and pre-trained models are available at https://github.com/matsuolab/BREMEN .

• The paper introduces deployment efficiency and the BREMEN algorithm, reducing policy deployments through ensemble dynamics models and trust-region updates.
• It demonstrates that BREMEN learns effective policies with only 5 to 10 deployments, surpassing traditional methods like SAC and ME-TRPO.
• The paper shows that BREMEN achieves competitive offline performance using smaller datasets, enabling safe and cost-effective real-world applications.

Deployment-Efficient Reinforcement Learning via Model-Based Offline Optimization

The paper "Deployment-Efficient Reinforcement Learning via Model-Based Offline Optimization" addresses a significant challenge in applying reinforcement learning (RL) in real-world environments: the cost and risk associated with deploying new data-collection policies. Traditional RL methods require frequent interaction with the environment, which is not feasible in domains like healthcare or robotics due to safety and cost constraints. The authors propose a novel measure called deployment efficiency, which focuses on reducing the number of distinct data-collection policies used during RL training.

The core contribution of this paper is the introduction of a model-based reinforcement learning algorithm named Behavior-Regularized Model-ENsemble (BREMEN). BREMEN aims to optimize policies with significantly fewer data and policy deployments than conventional methods, relying on model-based reinforcement learning principles. It effectively combines model-based dynamics learning with behavior regularization to address the challenges of deployment efficiency.

Key Contributions

1. Deployment Efficiency Concept: The paper defines deployment efficiency as the metric representing the number of times a policy is deployed for data collection in the RL process. This is distinct from sample efficiency, which focuses solely on the number of samples used rather than the deployment count. The measure is particularly relevant when deployment carries significant costs or risks.
2. BREMEN Algorithm: BREMEN employs a Gaussian policy initialized via behavior cloning, supported by a conservative trust-region optimization to mitigate the policy distribution shift challenge. A key technique is the ensemble of dynamics models utilized to generate imaginary rollouts, supporting efficient offline policy optimization.
3. Empirical Evidence: The authors present experimental evidence showing BREMEN's superior performance in terms of deployment efficiency. The approach achieves successful policy learning from scratch in only 5 to 10 deployments, even when compared to the state-of-the-art methods like Soft Actor-Critic (SAC) and Model-Ensemble Trust-Region Policy Optimization (ME-TRPO).
4. Offline RL Performance: Besides its deployment efficiency, BREMEN is evaluated in offline settings with static datasets. The method delivers competitive performance using smaller datasets compared to leading model-free methods such as Behavior Regularized Actor-Critic (BRAC) and Batch-Constrained Q-learning (BCQ).

Implications

1. Practical Application in Sensitive Domains: With the introduction of deployment efficiency and the effectiveness of the BREMEN algorithm, this research paves the way for RL applications in sensitive fields where data collection risks and costs are paramount.
2. Advancements in Model-Based RL: BREMEN demonstrates the viability of model-based approaches in scenarios traditionally dominated by model-free methods, setting the stage for future work in improving the scalability and robustness of model-based solutions in RL.
3. Exploration of Implicit KL Regularization: The use of implicit regularization through behavior cloning followed by trust-region updates suggests an effective strategy for maintaining the stability and performance of learned policies, opening avenues for exploration in regularization techniques for RL.

Future Prospects

The deployment efficiency introduced in this paper highlights a critical aspect of RL that future research needs to consider when assessing algorithm applicability to real worlds, such as robotics or autonomous systems. Continuing research could focus on enhancing model uncertainty estimation and exploring alternative implicit regularization methods to further reduce deployment requirements without sacrificing sample efficiency or policy performance. This work sets an important benchmark, providing a foundation for future advancements in safe and cost-effective RL deployment strategies.