Proxy Agent Training

• Proxy agent training is a multifaceted technique that uses surrogate models (e.g., proxy groups, simulators, rewards) to achieve fairness, sample efficiency, and privacy under limited direct supervision.
• It employs methodologies such as proxy group selection, constrained optimization, and simulation-based evaluation to refine agent performance and ensure alignment with desired outcomes.
• Practical applications include autonomous systems, federated learning, and meta-learning, demonstrating the method’s ability to improve real-world performance while addressing fairness and privacy concerns.

Proxy agent training encompasses methodologies that leverage surrogates—proxy groups, domains, reward functions, feature extractors, or memory constructs—to achieve desired properties (such as fairness, sample efficiency, safety, privacy, alignment, or generalization) in situations where direct signals or full supervision are unavailable or impractical. The following sections review foundational principles, methodologies, theoretical frameworks, and practical findings across representative research in this area.

1. Proxy Group Selection and Fairness Optimization

When explicit group labels (e.g., for protected attributes) are inaccessible, proxy groups defined via observable features that are hypothesized to be correlated with target properties can be used to optimize fairness metrics. Proxy groups may be constructed from categorical features such as last names (a proxy for ethnicity) or business attributes (e.g., chain/non-chain status correlating with region) (Gupta et al., 2018). Selection criteria emphasize maximal alignment: the overlap in membership between proxy and true groups positively influences the likelihood that optimizing fairness on proxies benefits the actual protected groups.

Three primary fairness metrics are addressed:

• Statistical Parity: $\Pr\{\hat{y} = 1 | G_k = 1\} = \Pr\{\hat{y} = 1\}$
• Equal Opportunity: $$\Pr\{\hat{y} = 1 | Y = 1, G_k = 1\} = \Pr\{\hat{y} = 1 | Y = 1\}$$
• Accurate Coverage: $$\Pr\{\hat{y} = 1 | G_k = 1\} = \Pr\{Y = 1 | G_k = 1\}$$

Two major workflows are employed:

• Constrained Optimization: Minimize prediction loss plus regularization, subject to fairness constraints imposed on proxy groups.
• Fairness Post-Processing: Augment models with proxy group-specific correction terms (e.g., additive $\beta_k$ per group).

Empirical results suggest the approach generalizes: improvements in fairness metrics obtained via proxy group optimization on training data are transferred to the true groups at test time, contingent on strong proxy-target alignment.

2. Proxy Domains and Simulation Usefulness

Proxy domains (often high-fidelity simulators or surrogate environments) serve dual functions in agent training: facilitating both predictive evaluation and data-efficient learning for embodied agents (Courchesne et al., 2021). The paper introduces formal metrics:

• Proxy Predictivity Value (PPV): Quantifies absolute difference in task-specific performance between proxy and target domains across evaluation metrics.
• Proxy Relative Predictivity Value (PRPV): Measures agreement in relative agent rankings between proxy and target domains.
• Proxy Learning Value (PLV): Captures reduction in real-world samples required when pre-training in proxy domains.

Proxy domain optimization involves minimizing PRPV (for predictive fidelity) and maximizing PLV (for learning efficiency), adaptively tuning simulator parameters. Task-conditional assessments are emphasized: proxy quality must be evaluated relative to the specifics of the task in question, as domain gaps may have variable significance across tasks.

3. Proxy Experience, Memory, and Privacy

In distributed or federated RL settings, direct sharing of experience memory between agents can compromise privacy. Proxy experience memory addresses this by exchanging only aggregated policy outputs over pre-arranged, clustered proxy states (Cha et al., 2019). This mechanism hides detailed trajectory data and individual decisions, reducing privacy risk and communication bandwidth requirements.

The federated reinforcement distillation framework employs advantage actor-critic networks, with proxy experience memory constructed as:

$\mathcal{M}^p = \{(s_k^p, \pi^p(a_k|s_k^p))\}_k$

where $s_k^p$ is a representative state and $\pi^p$ is the time-averaged policy. Experimental findings show that policy distillation via proxy experience memory achieves comparable sample efficiency and stability to direct experience sharing while maintaining privacy.

4. Proxy Reward Functions and Human Alignment

Proxy rewards are commonly used where explicit desiderata are hard to specify or when direct human feedback is sparse or imperfect. Two recent frameworks exemplify proxy-based agent alignment:

• Iterative Learning from Corrective actions and Proxy rewards (ICoPro): Alternates between incorporating human corrective actions (via Q-function margin loss) and learning from proxy rewards, regularized through pseudo-target labels for sample efficiency and stability (Jiang et al., 8 Oct 2024). The combination of imperfect but complementary signals yields policies more robustly aligned to human preferences.
• Proxy Value Propagation (PVP): During human-in-the-loop training, state-action pairs receive direct Q-value adjustments: desirable (human-demonstrated) actions are labeled with high values, while intervened (undesired) actions receive low values. Temporal difference loss propagates these proxy labels across the state-action space, driving policy induction toward human-like behaviors (Peng et al., 5 Feb 2025).

5. Proxy Feature Alignment and Active Learning

To improve active learning efficiency with large pretrained models, proxy-based methodologies utilize pre-computed feature representations for fast sample selection. However, performance may degrade when static proxies diverge from the fine-tuned model. The aligned selection via proxy (ASVP) method periodic updates pre-computed features and dynamically selects training modes (e.g., linear probing, full fine-tuning) to preserve pre-trained information and avoid redundant or missed critical samples (Wen et al., 2 Mar 2024). The resulting system achieves significant annotation cost savings with minimal computational overhead.

6. Proxy Agents and Meta-Learning

Under meta-learning paradigms, “helper” or “proxy” agents are jointly trained with a “prime” agent to dynamically adapt behavior in cooperative tasks—without access to explicit rewards or demonstrations (Woodward et al., 2019). Proxy agents infer task objectives from primary agent actions and emergent interaction dynamics. The meta-learned policies, instantiated as recurrent Q-networks, enable rapid adaptation over a distribution of tasks, exemplifying proxy training as a means to boost mutual performance in collaborative settings.

7. Limitations and Considerations

Proxy agent training’s general utility is critically dependent on alignment between the proxy signal/domain/entity and the target objective. Misalignment, excessive proxy constraints, or sparse/non-informative proxies can undermine generalization, induce overfitting, or deteriorate fairness as evidenced in both theoretical analyses and empirical studies (Gupta et al., 2018, Courchesne et al., 2021). Further, privacy-preserving proxy mechanisms necessitate careful design to balance compression and learning signal fidelity (Cha et al., 2019).

Appropriate selection of fairness, utility, or proxy metrics, together with regularization and update mechanisms (e.g., feature refreshing, task-specific simulator tuning, margin-based losses), is essential for robust proxy agent training. In human-involved settings, mechanisms to efficiently acquire, propagate, and balance corrective feedback with proxy signals remain an open area of research.

Summary Table: Proxy Agent Mechanisms

Mechanism	Signal/Proxy Type	Primary Application Domain
Proxy Groups	Grouped observable features	Fairness, downstream generalization
Proxy Domain	High-fidelity simulators	Robotics, evaluation, curriculum
Proxy Experience Memory	Clustered/aggregated states	Distributed RL, privacy preservation
Proxy Rewards/Values	Surrogate reward functions	RL alignment, human-in-the-loop RL
Proxy Features	Pre-computed representations	Active learning, efficient sampling
Meta-learned Helper	Coupled agent interactions	Collaboration, rapid adaptation

8. Practical Applications

Proxy agent training frameworks have enabled substantial advances in fairness optimization, privacy-preserving federated learning, simulation-based transfer, sample-efficient active learning, and safe RL via human feedback and alignment. Notable applications include college admissions screening, online recommendation, autonomous vehicles, robotics, and vision-based navigation.

In conclusion, proxy agent training is a multifaceted research area defined by leveraging indirect signals or models—via proxies—in situations characterized by incomplete supervision, resource constraints, or privacy requirements. Its theoretical foundations, algorithmic innovations, and demonstrated empirical benefits across diverse machine learning, RL, and agent-based domains underscore its importance for advancing robust, ethical, and practical AI systems.