Reinforcement Learning with Prototypical Representations (2102.11271v2)

Abstract: Learning effective representations in image-based environments is crucial for sample efficient Reinforcement Learning (RL). Unfortunately, in RL, representation learning is confounded with the exploratory experience of the agent -- learning a useful representation requires diverse data, while effective exploration is only possible with coherent representations. Furthermore, we would like to learn representations that not only generalize across tasks but also accelerate downstream exploration for efficient task-specific training. To address these challenges we propose Proto-RL, a self-supervised framework that ties representation learning with exploration through prototypical representations. These prototypes simultaneously serve as a summarization of the exploratory experience of an agent as well as a basis for representing observations. We pre-train these task-agnostic representations and prototypes on environments without downstream task information. This enables state-of-the-art downstream policy learning on a set of difficult continuous control tasks.

Reinforcement Learning with Prototypical Representations

The paper "Reinforcement Learning with Prototypical Representations" presents Proto-RL, a self-supervised framework for enhancing representation learning in reinforcement learning (RL) environments. The primary goal of this work is to address the intertwined nature of representation learning and exploration in image-based RL, where a lack of task-specific rewards complicates the process of obtaining informative representations and exploring effectively.

The central hypothesis of the research is leveraging prototypical representations, which serve as a dual function: as a summary of exploratory experiences and as a foundation for representing observations. This approach is designed to improve both the efficiency of subsequent policy learning and task-specific downstream exploration.

Key Contributions

1. Task-Agnostic Pre-training Scheme: Proto-RL employs a two-phase process. Initially, it uses a task-agnostic setup where the agent explores the environment without rewards, learning a representation that can generalize across different tasks within that environment. During this phase, the framework learns an image encoder and a set of prototypical embeddings, called prototypes.
2. Entropy-Based Exploration: The exploration policy is guided by intrinsic rewards generated through an entropy-based method, which uses particle-based entropy to incentivize covering unexplored regions of the state space. This is computed using nearest neighbor entropy estimation in the latent space defined by the learned prototypes.
3. Downstream Task Performance: The learned representations and prototypes are applied to downstream tasks, where Proto-RL excels by rapidly achieving a comprehensive state coverage and improving exploration efficiency, especially in environments with sparse rewards. This is validated across a suite of challenging continuous control tasks from the DeepMind Control Suite.
4. Prototypical Representations for Robust Exploration: These provide a structured latent space that facilitates the identification of novel states, allowing exploration strategies to focus more on novel experiences rather than exhaustive searches among high-dimensional input spaces.

Experimental Evaluation

Proto-RL's framework was evaluated on 16 challenging environments revealing its superior performance compared to baseline methods such as Curiosity-driven exploration and APT (Active Pre-Training) when conducted with task-agnostic pre-training. Notably, Proto-RL with 500k pre-training steps frequently surpassed the performance of DrQ (a task-specific approach trained with 1M steps).

The experiments also demonstrated that generalization across tasks within the same environment was notably enhanced. By freezing the pre-trained representations during downstream phases, Proto-RL achieved significant improvements in multi-task settings, a key advantage for real-world applications where tasks can vary significantly.

Implications and Future Directions

Practically, Proto-RL enhances the sample efficiency of RL systems in environments characterized by high-dimensional observations and sparse rewards, which is pivotal for domains like robotics where direct supervision is challenging. Theoretically, Proto-RL suggests new possibilities for disentangling representation learning from task-specific RL, allowing for better generalization and reduced data reliance.

The research opens several promising pathways, such as integrating these methods with model-based RL for even more efficient exploration and seeking theoretical insights into the structure and dynamics of learned discrete representations. Additionally, extending these principles to offline RL scenarios, where limited interaction is available, could significantly broaden the appeal and applicability of the Proto-RL framework.

The work sets a benchmark for leveraging self-supervised learning techniques within RL contexts, showcasing the benefits of prototypical representations in achieving efficient and scalable exploration strategies.