Clément Dumas, Walter Laurito, Robert Klassert, Kaarel Hänni

Epistemic Status: Initial Exploration

The following is a status update of a project started as part of the SPAR program. We explored some initial directions and there are still a lot of low-hanging fruits to pick up. We might continue to work on this project, either again as part of another SPAR iteration or with others who would be interested to work on this.

TL;DR

We adapted the Contrast Consistent Search (CCS) loss to find value-like directions in the activations of CNN-based PPO agents. While we had some success in identifying these directions at late layers of the critic network and with specific informative losses, we discovered that early layers and the policy network often contained more salient features that overshadowed the value-like information. In simple environments like Pong, it might be feasible to normalize the obvious salient features (e.g., ball position and approach). However, for more complex games, identifying and normalizing all salient features may be challenging without supervision. Our findings suggest that applying CCS to RL agents, if possible, will require careful consideration of loss design, normalization, and potentially some level of supervision to mitigate the impact of highly salient features.

Motivation

The research direction of "Searching for Search" investigates how neural networks implement search algorithms to determine actions. The goal is to identify the search process and understand the underlying objectives that drive it. By doing so, we may be able to modify the search to target new goals while maintaining the model's capabilities. Additionally, proving the absence of search could indicate limited generalization ability, potentially reducing the likelihood of deception.

A natural first step towards finding search in models is to examine a Reinforcement Learning agent and determine if we can identify the agent's estimate of the value of a state (or action). Typically, the value network outputs this value, while the policy network outputs an action. To output an action, we think that the policy network could probably require some internal representation of value. Therefore, based on an example from our mathematical framework, we employed both unsupervised and supervised probing methods to try to uncover the value of a state of a value network and p...

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