Learning Invariant Representations for Reinforcement Learning without Reconstruction (2006.10742v2)

Abstract: We study how representation learning can accelerate reinforcement learning from rich observations, such as images, without relying either on domain knowledge or pixel-reconstruction. Our goal is to learn representations that both provide for effective downstream control and invariance to task-irrelevant details. Bisimulation metrics quantify behavioral similarity between states in continuous MDPs, which we propose using to learn robust latent representations which encode only the task-relevant information from observations. Our method trains encoders such that distances in latent space equal bisimulation distances in state space. We demonstrate the effectiveness of our method at disregarding task-irrelevant information using modified visual MuJoCo tasks, where the background is replaced with moving distractors and natural videos, while achieving SOTA performance. We also test a first-person highway driving task where our method learns invariance to clouds, weather, and time of day. Finally, we provide generalization results drawn from properties of bisimulation metrics, and links to causal inference.

• The paper introduces R4L, a method that aligns latent representations with task-relevant state dynamics via bisimulation metrics.
• It demonstrates improved RL robustness by filtering out visually rich, irrelevant features from high-dimensional inputs.
• Experimental results on modified MuJoCo and highway driving tasks validate its ability to generalize and enhance policy performance.

Learning Invariant Representations for Reinforcement Learning without Reconstruction

The paper presents a novel approach to enhancing reinforcement learning (RL) from complex, high-dimensional observations such as images by learning invariant representations. It focuses on representation learning strategies that effectively separate task-relevant information from irrelevancies without the typical reliance on reconstruction, which can often include noise or distractor details problematic for subsequent RL tasks.

The central challenge addressed is accelerating RL by efficiently creating state representations that are invariant to task-irrelevant image features, mitigating the adverse effects of a visually rich but environmentally irrelevant background. The solution proposed leverages bisimulation metrics from continuous Markov Decision Processes (MDPs). Bisimulation metrics serve as a quantification of the behavioral similarity between states by capturing only those aspects of states most germane to the task at hand while disregarding extraneous details. This solution aligns distances in latent space as encoded by neural networks with bisimulation distances within MDP state spaces.

The authors introduce a novel method: robust representations without reconstruction for reinforcement learning (R4L). The method involves training encoders such that the latent space effectively represents task-relevant similarities and excludes task-irrelevant variations. This is achieved through learning encodings where distances mirror bisimulation distances, allowing subsequent RL algorithms to concentrate on pertinent information.

To test the robustness of R4L, experiments using modified MuJoCo environments are conducted, including variations in visual inputs such as cluttered backgrounds with moving distractors and natural videos. These illustrations demonstrate R4L's superior ability to maintain focus on task-relevant information and achieve state-of-the-art results despite background complexities. Further experimentation involving a highway driving task, replete with distracting environmental features like clouds and varying weather conditions, showcases the methodology's application within high-fidelity, dynamic environments.

The paper's results indicate significant robustness against non-task-related distractors, a notable improvement on conventional methodologies that employ pixel reconstruction or contrastive losses. Theoretical analyses further substantiate their claims by providing bounds on the optimality of value functions based on bisimulation metrics, and extend these insights to causal inference, where the method's invariant representations are demonstrated to generalize effectively to new tasks that share causal features with training tasks.

By employing an adept combination of bisimilarity principles with RL policy optimization, this work offers a potent advancement in representation learning applicable to RL. The approach illustrates how future developments in AI might efficiently distill task-critical features from visually complex inputs, potentially broadening the applicability of RL in real-world, visually-rich tasks where distinguishing pertinent details from distracting noise is imperative. The interlinkage with causal modeling is both a novel and considerable theoretical merit, offering avenues for cross-disciplinary growth within the fields of artificial intelligence and causal inference.

Prospective directions could involve further exploration into multi-task and lifelong learning frameworks, wherein models like R4L can dynamically adjust and refine their understanding of task relevance across varying contexts and temporal dynamics, offering yet more resilient and generalizable representations for complex real-world applications.