Agent-Centric Learning Objectives

• Agent-centric learning objectives are defined as methods that prioritize an agent's internal representational empowerment over external environmental rewards.
• They leverage dual-layer architectures—combining a curator for internal knowledge curation with an executor for task-level operation—to enhance sample efficiency and adaptability.
• Empirical case studies demonstrate benefits in diverse applications, from improved generalization in Minecraft tasks to robust performance in multi-agent social dilemmas.

Agent-centric learning objectives prioritize the agent’s own representational, structural, or multi-faceted control “from within,” as opposed to maximizing externally-defined environmental rewards. Advances in this domain formalize objectives in terms of intrinsic knowledge curation, discovery, or manipulation of internal and emergent capabilities, supporting preparedness, transferability, and robustness in intelligent agents and multi-agent systems.

1. Formalizations: Representational Empowerment and Internal Objective Design

Agent-centric objectives fundamentally shift the optimization locus from the external environment to the agent’s internal knowledge structures. The leading notion, representational empowerment (RepEmp), is defined as the channel capacity (mutual information) between the agent’s chosen internal operation sequence and the resulting variant of its own internal library of knowledge or representations. Mathematically, after $k$ tasks, given library $Z_k$ and primitive operations $\Omega$, the T-step operation sequence $\omega = (\omega^1, ..., \omega^T) \in \Omega^T$ transforms $Z_k$ to $Z'_k$ under transition $p(Z'_k|Z_k,\omega)$. Representational empowerment is then

$$\mathrm{RepEmp}(Z_{k}) = \max_{\omega\in \Omega^{T}} I(Z'_{k};\omega|Z_{k})$$

where $I(\cdot;\cdot|\cdot)$ denotes conditional mutual information. This value decomposes into a diversity term $H(Z'_k|Z_k)$ and an unpredictability penalty $H(Z'_k|Z_k, \omega)$, reflecting the controllable diversity within the internal knowledge substrate. Unlike traditional environmental empowerment $$\mathrm{EnvEmp}(s) = \max_{\pi(a_{1:T})} I(s';a_{1:T}|s)$$, RepEmp is agnostic to external state control and measures the agent's ability to reliably and flexibly modify its own internal knowledge base (Zhou et al., 29 Jul 2025).

Agent-centric designs can also include unsupervised clustering of encountered state/feature vectors to autonomously identify multiple objectives, with parallel off-policy learning per discovered objective (Karimpanal et al., 2017). In multi-agent domains, agent-centricity may take the form of weakly or strongly personalized auxiliary losses, prediction-based curiosity objectives, or differentiable games where the alignment between individual and collective objectives is treated as a subject of learning dynamics rather than fixed reward structure (Li et al., 2024, Reyes et al., 2022).

2. Architectural and Algorithmic Principles

Effective realization of agent-centric objectives demands a meta-layer that curates internal structure and a task-oriented executor that exploits this structure to maximize extrinsic (or downstream) reward. The highest-performing architectures implement a two-level scheme:

• Curator (Meta-level): Integrates new knowledge fragments, prunes redundancy, composes higher-level abstractions, and receives RepEmp-based intrinsic rewards.
• Executor (Task-level): Selects and applies short operation sequences to customize the library for a task, interleaving representational adjustments with policy learning (Zhou et al., 29 Jul 2025).

Operational primitives $\Omega$ may be symbolic (mutation, crossover, abstraction) or learned macro-operations. Distances between representations are often evaluated with functional metrics—e.g., based on behavioral differences under execution, which drive entropy estimation for RepEmp.

In user-centric interaction settings, tailored reward assignment and trajectory scoring mechanisms (e.g., turn-level reshaping, reward-to-go, exponential mappings) define agent-centric objectives through user interaction modalities and resulting feedback (Qian et al., 24 Sep 2025).

Multi-agent architectures increasingly utilize agent-centric modules, such as agent-specific attention mechanisms or unsupervised predictive losses, to learn better individual models and richer cooperation strategies. Joint agent-centric and global objectives are composed via specification primitives and decentralized automata with synchronization states to manage coordination cost (Shang et al., 2021, Eappen et al., 2022).

3. Multi-Objective and Multi-Agent Agent-Centricity

Multi-agent environments accentuate the necessity for agent-centric objectives due to the natural heterogeneity in goals. The multi-objective Markov game (MOMG) framework captures this by giving each agent a vector-valued reward at each timestep and framing solution concepts such as Pareto-Nash Equilibrium (PNE) and Pareto Correlated Equilibrium (PCE). Formally, PNE requires that no unilateral deviation allows an agent to strictly improve any component without a tradeoff, and PCE generalizes to correlated deviations (Wang, 27 Sep 2025).

Learning algorithms for MOMG reduce vector objectives to scalarized games via preference vectors $\lambda^k$ per agent, enabling solution by standard (single-objective) Nash or correlated equilibrium computation tools.

In mixed-motive cooperation (e.g., social dilemmas), agent-centric alignment algorithms such as Altruistic Gradient Adjustment (AgA) perform parametric blending between individual and collective gradients, augmented with custom consensus terms to steer learning toward stable equilibria of the collective objective, while preserving self-interest. AgA’s gradient direction is dynamically adjusted based on local geometric structure of the loss landscape, maintaining high social welfare and equality in empirical evaluations (Li et al., 2024).

4. Empirical and Theoretical Benefits

Adopting agent-centric objectives yields several documented benefits:

• Preparedness: Agents can rapidly adapt to novel tasks by recombining internal knowledge, with reduced additional environment interaction (Zhou et al., 29 Jul 2025).
• Sample Efficiency: Internal curation and auxiliary predictive training significantly accelerate policy learning and enhance generalization (Shang et al., 2021).
• Robustness: Avoidance of myopic reward-gaming and overfitting to transient or extrinsic objectives, with improved performance across shifting environments (Zhou et al., 29 Jul 2025).
• Decomposability and Curriculum Scaling: Distributed specification frameworks allow scalable training via curriculum on small agent groups and decentralized automata, mitigating combinatorial explosion (Eappen et al., 2022).
• Expressive Policy Classes: Agent-centric objectives facilitate interpretable symbolic structures, abstraction formation, and programmatic knowledge accumulation.

5. Open Challenges and Limitations

Despite their promise, agent-centric objectives pose distinct challenges:

• Information Estimation: Computing high-dimensional mutual information $I(Z';\omega|Z)$ for large knowledge spaces remains computationally intensive and sensitive to the choice of metric (syntactic vs. functional) (Zhou et al., 29 Jul 2025).
• Operation Set Design: Ensuring the operation set $\Omega$ is both expressive and tractable, and discovering/learning suitable macros, is a nontrivial engineering problem.
• Scalability: Real-world domains with continuous or high-dimensional internal representations challenge the scalability of representational empowerment and distributed monitoring schemes (Zhou et al., 29 Jul 2025, Eappen et al., 2022).
• Alignment Tuning: Hyperparameters governing individual-vs-collective tradeoff (e.g., AgA’s $\lambda$) are sensitive, and wrong settings can degrade performance or stall convergence (Li et al., 2024).
• Benchmarking: Most successes are in synthetic or moderately-complex domains; applicability to fully open-ended or real-world problems requires further empirical validation.

6. Illustrative Case Studies

Context	Agent-Centric Mechanism	Effect/Metric
Minecraft tasks	RepEmp library abstraction/pruning (Zhou et al., 29 Jul 2025)	Generalization across biomes
Multi-agent navigation	Two-headed curiosity module (Reyes et al., 2022)	Enhanced exploration, policy performance
Interactive RL	Turn/trajectory-level custom reward (Qian et al., 24 Sep 2025)	Efficient, user-aligned multi-turn learning
Social Dilemmas	Altruistic Gradient Adjustment (Li et al., 2024)	High social welfare, equality

These case studies demonstrate agent-centric objectives’ impact on knowledge diversity, multi-objective alignment, and real-world task transfer.

7. Outlook and Research Directions

The agent-centric paradigm reframes the pursuit of adaptive intelligence as the development and controllable diversification of internal representations and objectives, decoupled from narrow environmental signals. Future progress hinges on advances in scalable mutual information estimation, automated operational design, large-scale evaluation, and integration with broader frameworks for hierarchical or compositional reasoning. Bridging these technical frontiers is central to the trajectory toward robust general intelligence and resilient multi-agent cooperation (Zhou et al., 29 Jul 2025, Wang, 27 Sep 2025, Li et al., 2024).