Samuel Garcin

Studying the Interplay Between the Actor and Critic Representations in Reinforcement Learning

Trevor McInroe

Christopher G. Lucas

David Abel

Stefano V Albrecht

Extracting relevant information from a stream of high-dimensional observations is a central challenge for deep reinforcement learning agents… (voir plus). Actor-critic algorithms add further complexity to this challenge, as it is often unclear whether the same information will be relevant to both the actor and the critic. To this end, we here explore the principles that underlie effective representations for the actor and for the critic in on-policy algorithms. We focus our study on understanding whether the actor and critic will benefit from separate, rather than shared, representations. Our primary finding is that when separated, the representations for the actor and critic systematically specialise in extracting different types of information from the environment -- the actor's representation tends to focus on action-relevant information, while the critic's representation specialises in encoding value and dynamics information. We conduct a rigourous empirical study to understand how different representation learning approaches affect the actor and critic's specialisations and their downstream performance, in terms of sample efficiency and generation capabilities. Finally, we discover that a separated critic plays an important role in exploration and data collection during training. Our code, trained models and data are accessible at https://github.com/francelico/deac-rep.

2025-03-08

ArXiv (prépublication)

doi.org

arxiv.org

Studying the Interplay Between the Actor and Critic Representations in Reinforcement Learning

Samuel Garcin

Trevor McInroe

Pablo Samuel Castro

Christopher G. Lucas

David Abel

Prakash Panangaden

Stefano V Albrecht

Extracting relevant information from a stream of high-dimensional observations is a central challenge for deep reinforcement learning agents… (voir plus). Actor-critic algorithms add further complexity to this challenge, as it is often unclear whether the same information will be relevant to both the actor and the critic. To this end, we here explore the principles that underlie effective representations for an actor and for a critic. We focus our study on understanding whether an actor and a critic will benefit from a decoupled, rather than shared, representation. Our primary finding is that when decoupled, the representations for the actor and critic systematically specialise in extracting different types of information from the environment---the actor's representation tends to focus on action-relevant information, while the critic's representation specialises in encoding value and dynamics information. Finally, we demonstrate how these insights help select representation learning objectives that play into the actor's and critic's respective knowledge specialisations, and improve performance in terms of agent returns.

2025-01-22

ICLR.cc/2025/Conference (poster)

openreview.net