State Representation Learning for Control: An Overview (1802.04181v2)

Abstract: Representation learning algorithms are designed to learn abstract features that characterize data. State representation learning (SRL) focuses on a particular kind of representation learning where learned features are in low dimension, evolve through time, and are influenced by actions of an agent. The representation is learned to capture the variation in the environment generated by the agent's actions; this kind of representation is particularly suitable for robotics and control scenarios. In particular, the low dimension characteristic of the representation helps to overcome the curse of dimensionality, provides easier interpretation and utilization by humans and can help improve performance and speed in policy learning algorithms such as reinforcement learning. This survey aims at covering the state-of-the-art on state representation learning in the most recent years. It reviews different SRL methods that involve interaction with the environment, their implementations and their applications in robotics control tasks (simulated or real). In particular, it highlights how generic learning objectives are differently exploited in the reviewed algorithms. Finally, it discusses evaluation methods to assess the representation learned and summarizes current and future lines of research.

• The paper presents a comprehensive survey of SRL methods applied to robotic control, highlighting diverse learning objectives and techniques.
• The paper details models such as autoencoders, forward, inverse, and adversarial networks to effectively reduce high-dimensional data.
• The paper emphasizes challenges in evaluating representations with metrics like Distortion and NIEQA, and outlines future research for adaptive control systems.

State Representation Learning for Control

The paper "State Representation Learning for Control: An Overview" by Timothée Lesort, Natalia Diaz-Rodriguez, Jean-Francois Goudou, and David Filliat offers a thorough survey of methodologies in state representation learning (SRL), particularly focusing on their utility in control tasks for robotics. The authors provide an extensive review of the existing SRL methods, their application in robotics control tasks, and the learning objectives these methods employ. This survey also discusses evaluation strategies for assessing learned representations and speculates on future research trends in the domain.

Key Components of State Representation Learning

State representation learning is a subset of representation learning algorithms designed to extract low-dimensional, temporal, and action-influenced features from high-dimensional observation data. This is particularly relevant in domains where raw data is prohibitively large, such as in robotics where sensor data (e.g., camera feeds) needs to be distilled into state representations that are more manageable for policy-making algorithms like reinforcement learning (RL).

The advantage of SRL lies in overcoming the curse of dimensionality, which is pivotal in improving policy learning efficiency both in terms of performance and computation speed. This becomes increasingly important in robotics, where physical experiments (like executing an action) are costly.

SRL Methods and Their Objectives

The paper categorizes various SRL approaches, each employing different objectives for state representation learning:

1. Reconstruction-Based Methods: These methods typically utilize autoencoders to ensure the learned state can reconstruct the input observation. The constraint is usually a compressed latent space that facilitates the reproduction of input data.
2. Forward Models: These methods predict future states from current states and actions, encouraging the encoding of dynamic aspects of the environment into the state representation.
3. Inverse Models: Here, the goal is to infer the action by examining the transition between the current and next states, thus emphasizing enough state encoding to determine causative actions.
4. Use of Priors and Additional Constraints: Models can be augmented with prior knowledge to impose additional constraints, thereby enforcing principles like temporal continuity or physical coherence in state representations.
5. Adversarial and Reward-Based Learning: Some approaches integrate adversarial networks to disentangle and refine latent representations, while others use rewards as auxiliary signals to embed task-related information into the learned states.

Evaluation and Applications

The evaluation of state representations is challenging due to the lack of standardized metrics. Currently, task performance in RL settings is a common method of evaluation, but it requires extensive experiments and multiple RL algorithms for reliable comparison. The paper discusses several metrics like Distortion, NIEQA, and KNN-MSE, which assess how well the learned representations retain structure and neighborhood properties compared to ground truth data.

Practically, SRL is used to facilitate efficient policy learning in applications like autonomous vehicles, robotics with high-dimensional sensor inputs, and environments requiring multimodal data integration. The learned representations can also accelerate transfer learning by utilizing a state space efficient for multiple tasks.

Implications and Future Directions

The practical implications of SRL are vast, particularly in making autonomous agents more efficient and interpretable. SRL reduces the dimensionality of decision-making parameters, aiding in more transparent and explainable AI models. Future trends may focus on optimizing exploration strategies directly through SRL and enhancing the continual learning capabilities of these systems, paving the way for more adaptive and flexible robotic systems.

In summary, the paper provides a comprehensive assessment of the state of SRL, emphasizing the methodologies, evaluation techniques, practical applications, and potential future research directions. It serves as a valuable resource for researchers aiming to enhance the efficiency and efficacy of autonomous control systems.