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Attention Schema in Neural Agents
Dianbo Liu, Samuele Bolotta, He Zhu, Yoshua Bengio, Guillaume Dumas
TL;DR
The paper asks whether modeling attention can improve agents’ control of limited processing resources and support coordination by helping them infer other agents’ attention. It tests five attention–internal-control arrangements in cooperative multi-agent reinforcement learning and reports that the AST-aligned arrangement, in which internal control models attention, performs best. The experiments are preliminary and use 2D environments.
Problem
Attention Schema Theory predicts that a model of attention can support control of an agent’s own attention and inference about other agents’ attention, but the paper investigates these predictions in artificial agents.
Source: §1 · p. 1Method
The authors implement internal control as a recurrent network and attention as a key-value mechanism, then compare five relationships between them in cooperative multi-agent reinforcement-learning tasks.
Source: §1 · p. 2Results
Takeaways & Limitations
The authors suggest that modeling one’s own attention may aid decision making amid distractions, while modeling others’ attention may be useful in settings involving opponents, value alignment, or communication.
Source: §7 · p. 8The experiments are limited to 2D environments; the authors plan to test the architectures in richer environments, including 3D settings and robots.
Source: §7 · p. 8
Abstract
from arXiv · showhide
Attention has become a common ingredient in deep learning architectures. It adds a dynamical selection of information on top of the static selection of information supported by weights. In the same way, we can imagine a higher-order informational filter built on top of attention: an Attention Schema (AS), namely, a descriptive and predictive model of attention. In cognitive neuroscience, Attention Schema Theory (AST) supports this idea of distinguishing attention from AS. A strong prediction of this theory is that an agent can use its own AS to also infer the states of other agents' attention and consequently enhance coordination with other agents. As such, multi-agent reinforcement learning would be an ideal setting to experimentally test the validity of AST. We explore different ways in which attention and AS interact with each other. Our preliminary results indicate that agents that implement the AS as a recurrent internal control achieve the best performance. In general, these exploratory experiments suggest that equipping artificial agents with a model of attention can enhance their social intelligence.
1. Introduction
The section frames attention schema as a model of attention that could regulate an agent’s own attention and help it infer other agents’ attention. It introduces five possible relationships between attention and internal control, tested in cooperative multi-agent tasks.
1. Introduction
- The paper frames attention as dynamical information-flow control and considers a higher-order model of attention as an additional control layer.The passage distinguishes static control by weights from dynamic control by attention.Source: §1 · p. 1
- Attention schema theory proposes that an attention schema represents attention and its changing state, and can model other agents’ attention.The paper investigates whether attention remains possible without an attention schema, and whether an agent can use its own schema to infer other agents’ attention.Source: §1 · p. 1
- The fifth hypothesis, with internal control regulating attention, achieved higher performance than the alternatives in the preliminary experiments.The paper presents this as the AST-aligned relationship between attention and internal control.Source: §1 · p. 2
- The authors ask whether self-monitoring can improve agents’ control and deployment of limited processing resources.They position the work as a foundation for future research rather than a solution to the full challenge of building rich, flexible attention models for AI.Source: §1 · pp. 1–2
2. Internal control and attention
The section compares five hypotheses for how attention and internal control might interact, motivated by their roles in directing information and behavior. The AST-aligned hypothesis models attention dynamically and lets internal control regulate it.
2. Internal control and attention
- The five hypotheses examine how attention, its representation, and behavior relate in a control problem.The section introduces them as alternatives for exploring that relationship.Source: §2 · p. 2
2.1. Internal control and attention are the same
- Hypothesis 1 treats attention and internal control as the same process, so it has no separate internal-control module.The setup serves as a baseline; the passage notes that attention without active internal control has been associated with poorer performance in humans.Source: §2.1 · p. 2
2.2. Internal control precedes attention
- Hypothesis 2 puts internal control before attention, so the system can focus only on what has already been modeled.The passage identifies potential binding problems and weaker stimulus signals without attention.Source: §2.2 · p. 2
2.3. Attention precedes internal control
- Hypothesis 3 lets attention precede internal control, but the model is not recurrent and may be less flexible in changing environments.Only information under the focus of attention can be modeled in this setup.Source: §2.3 · p. 2
2.4. Internal control and attention are independent processes
- Hypothesis 4 runs internal control and attention in parallel, although the passage argues independent processes are unlikely to perform well.Both processes act on the input at the same time.Source: §2.4 · p. 2
2.5. Internal control of attention: the attention schema
- Hypothesis 5 models attention dynamically and lets internal control regulate it, a setup expected to offer the greatest coordination potential.The paper describes this as its closest representation of AST.Source: §2.5 · p. 2
3. Background and notation
This section introduces notation for a multi-agent reinforcement-learning game and points to hypotheses about the relationship between attention and internal control.
3. Background and notation
- A multi-agent reinforcement-learning game is represented by the tuple (N, S, A, P, R, O, γ).The passage names these as game components and begins defining them.Source: §3 · p. 2
- N denotes the set of agents, while S_t and O_t denote the agents’ states and partial observations at time t; observations may differ from states.The passage also defines actions, state transitions, and rewards for the game.Source: §3 · p. 2
- Figure 1 frames hypotheses about the relationship between attention and internal control.Source: Figure 1 · p. 3
4. Methods
The paper tests five ways of relating attention to recurrent internal control in multi-agent reinforcement learning. The hypotheses differ in whether control is absent, precedes or follows attention, runs independently, or models and controls attention.
4. Methods
- Hypotheses 1–5 were tested in two multi-agent reinforcement-learning tasks using architectures trained with proximal policy optimization.The methods passage names GhostRun and the Multi-Agent Particle environment as the two tasks.Source: §4 · p. 3, §4.1 · p. 3
4.1. Architectures
- Attention uses multi-head layers, while internal control uses GRUs; shared components keep the architectures comparable without sharing parameters.Internal-control inputs vary by hypothesis, and attention can select from observations or, in Hypotheses 2 and 4, from internal-control output.Source: §4.1 · p. 3
Attention module
- The attention module produces h1 as a weighted sum of values, with queries, keys, and values varying across hypotheses.The attention query strategy uses the whole observed view, averaged across patches.Source: Attention module · p. 3, §4.2 · p. 3
4.2. Hypothesis 1
- Hypothesis 1 equates attention with internal control: it has no separate control module and feeds attention output directly to the policy.Its query is based on the whole view, represented by averaging the observation across patches.Source: §4.2 · p. 3
4.3. Hypothesis 2
- Hypothesis 2 places internal control before attention, using its output as attention input and sending attention output to the policy.Source: §4.3 · p. 5
4.4. Hypothesis 3
- Hypothesis 3 places attention before internal control, feeding attention output to the control module and its output to the policy.Source: §4.4 · p. 5
4.5. Hypothesis 4
- Hypothesis 4 runs attention and internal control independently on observations, sending both outputs directly to the policy.Source: §4.5 · p. 5
4.6. Hypothesis 5
- Hypothesis 5 treats internal control as an attention schema: its RNN predicts attention output, while a generated binary mask gates attention scores.The mask is formed by activator and suppressor MLPs followed by Gumbel softmax; contrastive loss trains the prediction.Source: §4.6 · p. 5
5. Experimental design and preliminary results
The exploratory experiments compare attention and internal-control arrangements in cooperative multi-agent tasks. Hypotheses 5.4 and 5.5 show the best results, with preliminary results indicating marginal advantages for Hypothesis 5 overall.
5. Experimental design and preliminary results
- The Hypothesis 5 variants remove the mask, apply it to action distributions or attention output, or use the internal-control prediction as policy input.Source: §5 · p. 5
- Hypotheses 5.4 and 5.5 show the best exploratory results, while preliminary results indicate marginal advantages for Hypothesis 5 over the other hypotheses.Architecture 5.4 is the only version described as exerting control on attention.Source: §5 · p. 5
6. Experiments
The experiments compared five attention and internal-control hypotheses in cooperative multi-agent reinforcement learning. Preliminary results favor Hypothesis 5 in GhostRun and MazeCleaners.
6.1. Comparison between the five main hypotheses
- Hypothesis 5 achieved the best performance among the five main GhostRun hypotheses in both IID and OOD tests.Figure 6 compares rewards in the final 100 episodes across 10 random seeds.Source: Figure 6 · p. 6, §6.1 · p. 6
6.2. Ability to generalize in a continuous learning setting
- GhostRun continual-learning tests increased task difficulty every 50 episodes by adding another ghost.The comparison examined performance as the task difficulty changed over episodes.Source: §6.2 · p. 6
6.3. Replication in different task and environment
- In MazeCleaners, Hypothesis 5.4 also gave the best performance among all architectures.This replicated the main GhostRun results in a different task and environment.Source: §6.3 · p. 6
7. Discussion and Future Work
The discussion presents internal control that models attention as a coordination aid, while stressing that the experiments are preliminary and leave important questions about scope and model capacity open.
Discussion
- Experiments in multi-agent reinforcement learning support the conclusion that internal control modeling attention helps agents coordinate toward a common goal.The paper compared five possible relationships between attention and internal control.Source: §7 · p. 6
Future work
- The authors say further work must test internal attention control across varied environments and algorithmic components.They position their study as a decentralized approach in a direction also pursued through shared central critics and joint attention.Source: §7 · pp. 6, 8
Discussion
- In continual GhostRun learning, rewards commonly decrease as episodes add more ghosts to escape.The figure caption attributes the decline to increasing task difficulty.Source: Figure 7 · p. 7
Limitations
- The experiments used only controlled 2D tasks; testing in richer 3D environments, photorealistic game engines, and robots is future work.Source: §7 · p. 8
- The internal control is a traditional recurrent neural network, not yet a rich, coherent, descriptive, and predictive model of attention.The authors say it nevertheless incorporates core functional ideas of an attention schema.Source: §7 · p. 8
- The study did not test linguistic or other high-level cognitive benefits, or whether the module represents and predicts other agents’ attention.Source: §7 · p. 8
Potential applications
- The authors identify opponent modeling, value alignment, and communication as settings where inferring other agents’ attention may be useful.Source: §7 · p. 8