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Abstract

Shard Theory posits that reinforcement learning agents can be modeled as collections of contextually activated decision influences, or "shards." However, the mechanistic definition of shards remains an open question. This study investigates the contextual activation claim of Shard Theory using a maze-solving policy network. We hypothesize that the agent's cheese-seeking behavior is spatially localized within the maze environment, with a specific area of influence surrounding the cheese's location where its presence significantly affects the agent's decision-making. Through a series of experiments involving empty mazes, strategic cheese placements, and neural network probing, we analyze the policy's behavior and internal representations. Our findings suggest that while all network layers consistently encode the presence of cheese, the effect of this information on the agent's behavior is spatially localized within the maze environment. The influence of cheese proximity is most pronounced in later network layers, particularly the fully connected layer preceding the value head. These results offer insights into how shard-like structures might be represented within the network and help us better understand how reinforcement learning agents make context-dependent decisions.

Introduction

Shard Theory posits that reinforcement learning "agents are well modeled as being made of shards – contextually activated decision influences" (LawrenceC, 2022). The idea of shards aims to conceptualize how an agent's internal computations assign value to learned concepts through reinforcement, and subsequently how these values guide behavior. The notion of "contextual activation" suggests that specific environmental cues or internal states trigger the activation of relevant shards, influencing the agent's decision-making process.

Despite its potential explanatory power, the precise definition of a shard remains unclear in current literature. Shard Theory is often illustrated through examples, such as Turner and Pope's description of a baby developing a "juice-shard" through reinforcement learning. However, a rigorous mechanical definition of shards and their neural or cognitive representations in reinforcement learning agents has yet to be thoroughly explored, representing a significant gap in the field.

Shard Theory, if cor...

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