Offline Reinforcement Learning from Images with Latent Space Models (2012.11547v1)

Abstract: Offline reinforcement learning (RL) refers to the problem of learning policies from a static dataset of environment interactions. Offline RL enables extensive use and re-use of historical datasets, while also alleviating safety concerns associated with online exploration, thereby expanding the real-world applicability of RL. Most prior work in offline RL has focused on tasks with compact state representations. However, the ability to learn directly from rich observation spaces like images is critical for real-world applications such as robotics. In this work, we build on recent advances in model-based algorithms for offline RL, and extend them to high-dimensional visual observation spaces. Model-based offline RL algorithms have achieved state of the art results in state based tasks and have strong theoretical guarantees. However, they rely crucially on the ability to quantify uncertainty in the model predictions, which is particularly challenging with image observations. To overcome this challenge, we propose to learn a latent-state dynamics model, and represent the uncertainty in the latent space. Our approach is both tractable in practice and corresponds to maximizing a lower bound of the ELBO in the unknown POMDP. In experiments on a range of challenging image-based locomotion and manipulation tasks, we find that our algorithm significantly outperforms previous offline model-free RL methods as well as state-of-the-art online visual model-based RL methods. Moreover, we also find that our approach excels on an image-based drawer closing task on a real robot using a pre-existing dataset. All results including videos can be found online at https://sites.google.com/view/lompo/ .

• The paper presents LOMPO, which condenses high-dimensional image data into compact latent states for effective offline RL.
• It integrates uncertainty quantification using ensemble methods to penalize high model bias in latent dynamics.
• Experimental results show LOMPO consistently outperforms model-free methods in complex tasks, including real-world robotics.

Overview of "Offline Reinforcement Learning from Images with Latent Space Models"

The paper "Offline Reinforcement Learning from Images with Latent Space Models" presents an approach to address the challenges posed by offline reinforcement learning (RL) in environments with high-dimensional observational inputs, such as images. This research builds upon the model-based framework and focuses on extracting latent dynamics to efficiently learn policies from pre-collected datasets, circumventing the inherent risks of active data collection through online exploration.

Key Contributions

The main methodological advancement introduced by the authors is the Latent Offline Model-Based Policy Optimization (LOMPO) algorithm. LOMPO is designed to effectively handle systems where observations come primarily from visual data, extending the applicability of offline RL to complex domains like robotics. The paper highlights the importance of:

1. Latent State Representation: It introduces a technique to condense high-dimensional image data into compact latent states for more feasible modeling of system dynamics.
2. Uncertainty Quantification: LOMPO incorporates uncertainty estimation in the latent space to manage the inherent model bias, using ensemble methods to ascertain areas of high model uncertainty.
3. Policy Learning with Penalization: The algorithm leverages a pessimistic policy optimization approach by incorporating a penalization term for model uncertainty, aligning the learned policy closer to its expected utility in the true environment.

Experimental Validation

The effectiveness of LOMPO is demonstrated through rigorous experimentation across various challenging image-based tasks, including locomotion and robotic manipulation simulations, and a real-world robotic drawer-closing task:

• Performance: The algorithm consistently outperformed existing offline model-free RL methods and matched or exceeded state-of-the-art online visual model-based RL methods.
• Dataset Characteristics: LOMPO showed robustness to varying conditions of dataset size and quality, maintaining superior performance even with suboptimal or limited datasets typical of offline settings.

The empirical results are compelling, especially in demonstrating LOMPO's ability to execute complex tasks with high visual input reliance, like vision-based drawer closure, achieving a significant success rate with a real robot.

Implications and Future Work

The research advances the capabilities of offline RL in visually complex environments, which is critical for deploying RL in real-world applications where safety and data collection constraints are paramount. Practically, the use of latent spaces and uncertainty penalization mechanisms can significantly reduce the computational burden and enhance policy reliability.

Theoretically, these results suggest potential exploration into more refined uncertainty estimation techniques and dynamic latent representation learning. Future work could involve extending LOMPO to multitask scenarios or further refining scalability and generalization in even more diverse and unstructured environments. This avenue could transform the landscape of offline RL in domains necessitating high-dimensional perception and decision-making such as autonomous driving and healthcare.