DeepAveragers: Offline Reinforcement Learning by Solving Derived Non-Parametric MDPs (2010.08891v1)

Abstract: We study an approach to offline reinforcement learning (RL) based on optimally solving finitely-represented MDPs derived from a static dataset of experience. This approach can be applied on top of any learned representation and has the potential to easily support multiple solution objectives as well as zero-shot adjustment to changing environments and goals. Our main contribution is to introduce the Deep Averagers with Costs MDP (DAC-MDP) and to investigate its solutions for offline RL. DAC-MDPs are a non-parametric model that can leverage deep representations and account for limited data by introducing costs for exploiting under-represented parts of the model. In theory, we show conditions that allow for lower-bounding the performance of DAC-MDP solutions. We also investigate the empirical behavior in a number of environments, including those with image-based observations. Overall, the experiments demonstrate that the framework can work in practice and scale to large complex offline RL problems.

• The paper introduces the DAC-MDP framework that creates finite non-parametric MDPs from static datasets to enhance offline reinforcement learning.
• It demonstrates scalable performance in complex environments, including Atari games, through efficient GPU-accelerated value iteration.
• Theoretical analysis and empirical results validate DAC-MDP’s robustness and adaptability for offline RL with deep representations.

Overview of "DeepAveragers: Offline Reinforcement Learning by Solving Derived Non-Parametric MDPs"

This paper introduces a novel approach to offline reinforcement learning (RL) through the lens of non-parametric Markov Decision Processes (MDPs). The central contribution is the Deep Averagers with Costs MDP (DAC-MDP), which leverages a finite representation derived from static datasets to optimize RL tasks. This framework allows for flexibility in addressing various objectives and adapting to changes in environments without further learning.

Key Contributions

1. DAC-MDP Framework: The DAC-MDP model is a non-parametric approach that utilizes a static experience dataset to create a finite tabular MDP. It draws on deep representations while addressing data limitations by incorporating costs for transitions in under-explored regions. The core of DAC-MDP is the ability to define this rich structure while remaining computationally feasible.
2. Empirical Results and Scalability: The paper presents empirical results demonstrating the feasibility of DAC-MDPs across multiple environments, including those with image-based observations. Notably, the ability to scale to complex problems, such as those in Atari games, underscores its practical utility.
3. Theoretical Insights: The authors provide a theoretical analysis that outlines conditions for lower-bounding DAC-MDP's performance. This includes assumptions on the Lipschitz continuity of the BeLLMan operator, offering insights into the model's robustness.
4. Practical Implementations: A GPU implementation of value iteration is developed to handle the derivation and solution of large MDPs efficiently. This implementation is crucial for scaling the approach to large datasets.
5. Versatile Applications: DAC-MDPs demonstrate adaptability in varied scenarios, such as changes in action spaces and optimization of different planning horizons. This flexibility is a significant advantage over traditional RL methods, which often require retraining in new settings.

Numerical Results and Validation

The experimental results confirm the capability of DAC-MDPs to deliver competitive performance. In Atari-scale simulations, DAC-MDPs utilizing deep representations showed improved or comparable performance relative to state-of-the-art methods like DQN and BCQ, even under strict offline conditions. Moreover, the approach effectively handled data from different policies without retraining, showcasing its robust adaptability.

Implications and Future Directions

The implications of this work for model-based RL are notable. By integrating representation learning with optimal planning, DAC-MDPs pave the way for more efficient data utilization in offline scenarios. This work invites further exploration into specialized representation learning techniques designed for DAC-MDPs, aiming to refine their applicability in more complex domains.

Long-term, this framework could influence the development of RL systems that require minimal interaction with the environment, have enhanced generalization capabilities, and possess the flexibility to quickly adapt to new tasks or environments without exhaustive retraining.

In summary, the DAC-MDP framework offers a compelling direction for offline reinforcement learning, harmonizing theoretical rigor with practical viability and setting a foundation for future research in scalable, adaptive RL systems.