Cross-Environment Agent Learning

• Cross-environment agent learning is the study of designing agents to acquire, retain, and transfer skills across varied and dynamic environments.
• It integrates reinforcement learning, meta-learning, curriculum strategies, and multi-agent techniques to mitigate catastrophic forgetting and enhance runtime adaptation.
• Scalable benchmarks and adaptive meta-controllers are key to developing robust agents that generalize well across heterogeneous tasks.

Cross-environment agent learning is the paper and engineering of autonomous agents capable of acquiring, retaining, and transferring competencies across a suite of heterogeneous environments—each with distinct dynamics, observation modalities, reward structures, and tasks. Unlike conventional approaches that assume a fixed or narrowly parameterized environment, cross-environment agent learning explicitly addresses generalization under environment distribution shift, catastrophic forgetting, knowledge transfer, and runtime adaptation. This area synthesizes methods from reinforcement learning (RL), unsupervised environment design (UED), curriculum learning, meta-learning, and multimodal agent architectures, unified by the goal of inducing robust, generalist policies or learning systems operational across diverse worlds.

1. Foundational Concepts and Formal Definitions

A cross-environment learning setup defines a family of RL environments

$$\mathcal{E}_i = (\mathcal{S}_i, \mathcal{A}_i, T_i, R_i, \Omega_i, \tau_i)$$

where each environment $\mathcal{E}_i$ may supply unique state space, action primitives, transition/reward kernels, observation channels, or termination criteria. The agent faces a distribution $P_{\rm env}$ over such environments and is required to learn a policy, learning algorithm, or modular policy ensemble that maximizes aggregate performance across this distribution, often under resource or sample constraints.

Distinct operationalizations include:

• Policy-level generalization: train a universal $\pi_\theta$ (or population) to maximize $$J = \mathbb{E}_{\mathcal{E}\sim P_{\rm env}}[\mathbb{E}_{\tau\sim\pi_\theta,\mathcal{E}}[\sum_{t} \gamma^t r_t]]$$.
• Learning-algorithm generalization: meta-learn a procedure $f$ mapping episodic interaction data from $\mathcal{E}_i$ to an effective task-specific $\pi_{\theta,i}$, aiming for rapid adaptation ("few-shot" or "L2L" meta-RL).
• Component-centric agent improvement: treat agent learning as a loop of Selection, Optimization, and Evaluation over internal agent components (prompt, code, network), iterating these stages with respect to a suite of environments (Zhang et al., 24 Nov 2025).

This setting is orthogonal to classical domain randomization or domain adaptation: cross-environment agent learning emphasizes heterogeneity (non-parametric variation, e.g., different rules, modalities) and scalability (valuable benchmarks exhibit combinatorial diversity, e.g., AutoEnv-36 with 36 environments and 358 levels (Zhang et al., 24 Nov 2025)).

2. Agent Architectures and Systematic Approaches

2.1 Multi-agent and Population-based Approaches

• Eco-system of Agents: Maintain a dynamically-pruned pool of per-environment specialist agents $A = \{a_1,\dots,a_N\}$, each with its policy $\pi_i$ and coverage set $\text{solvedEnv}_i$; on a new environment, reuse existing agents whenever possible, spawn new agents only on miss, and prune those strictly subsumed in capabilities (Moulin et al., 2022). This approach sidesteps catastrophic forgetting and exploits opportunistic generalization discovered empirically.
• Role-free Multi-Agent Systems with Credit Assignment: CollabUIAgents employs $n$ homogeneous policies in a message-passing DAG, with rewards assigned by a process-level LLM-based critic rather than environment-specific signals, supporting dense, transferable feedback and improved generalization under communication structure randomization (He et al., 20 Feb 2025).
• Cross-Apprenticeship Frameworks: CAL optimizes for per-environment imitation while regularizing all policies to be close to a central policy, trading off environment-specific performance with global generalizability via a parametric $\|\pi_i-\pi_c\|_\infty\leq\epsilon$ constraint (Aravind et al., 2022).

2.2 Meta-Learning and Component-Centric Optimization

• AutoEnv's Selection–Optimization–Evaluation Formalism: Encapsulate each agent as a composite of internal components (“candidate” $c$), and iteratively improve components (prompt, code) by selection (e.g. best, Pareto), optimization (dynamics- or instruction-based signals), and evaluation (trajectory, reward) across variable environments (Zhang et al., 24 Nov 2025).
• Model-influenced Exploration for Adaptation: EMMA agents optimize not only for primary task reward but also to maximize their coverage of “interesting” regions (e.g. regions of high external-model uncertainty), thereby improving external model adaptation after environment shift (Bhagat et al., 28 Jun 2024).

2.3 Cross-environment Generalization in Multi-agent and Cooperative Settings

• Cross-Environment Cooperation (CEC): In zero-shot coordination, policies are trained via self-play over a procedurally generated ensemble of tasks, learning coordination conventions that generalize both to novel environments and unseen partners (as measured by cross-play and human–AI evaluations) (Jha et al., 17 Apr 2025).
• Joint Curriculum Learning for Multi-agent Games: MAESTRO constructs a bivariate curriculum over environment parameters and co-player policies to minimize the student’s worst-case regret, provably attaining Nash or Bayesian Nash equilibria in the induced metagame (Samvelyan et al., 2023).

2.4 Heterogeneous and Multimodal Agent Structures

• Specialized State Encoding and Alignment Losses: In multi-agent coverage tasks with heterogeneity (e.g. varied sensor fields), fixed-size encoding networks with triplet losses ensure agent observations can be mapped to a shared latent space, sustaining transferability despite heterogeneity (Wakilpoor et al., 2020).
• Multimodal Language Agents and Benchmarks: CRAB provides a graph-based, fine-grained evaluation for agents acting across desktop and mobile environments, pushing agents to robustly ground actions and partial progress in mixed-domain workflows (Xu et al., 1 Jul 2024).
• LLM-based Embodied Planning via Code Transfer: EnvBridge leverages memory-based transfer and in-context adaptation of robot-control code fragments, enabling code- and plan-level reuse for data-driven cross-environment control (Kagaya et al., 22 Oct 2024).

3. Environment Distribution Generation, Diversity, and Benchmarking

3.1 Automated and Procedurally Generated Environments

• AutoEnv Pipeline: Environments are defined as factorizable distributions over transitions, rewards, and observations: $$P(T, \Omega, R) = P_{\rm dyn}(T)\cdot P_{\rm obs}(\Omega|T)\cdot P_{\rm rew}(R|T)$$ and are generated via DSL specification, LLM-driven self-repair, and multi-stage validation, enabling low-cost creation of suites of highly diverse, validated worlds (Zhang et al., 24 Nov 2025).
• Unsupervised Environment Design (UED) and Diversity Metrics: Adaptive curricula such as DIPLR maintain a buffer of levels selected by policy-aware diversity (e.g. Wasserstein distance over occupancy measures), which demonstrably improves zero-shot generalization compared to regret- or GAE-only strategies (Li et al., 2023).
• Multimodal and Cross-platform Benchmarks: CRAB’s GDT (Graph of Decomposed Tasks) and AutoEnv-36’s broad coverage illustrate modern trends toward benchmarks that span devices, modalities, and task-composition logics (Xu et al., 1 Jul 2024, Zhang et al., 24 Nov 2025).

3.2 Scaling, Fidelity, and Evaluation

• GEF (Generation–Execution–Feedback) Loop: Scaling agent intelligence requires environments to generate diverse tasks (graph-structured, hierarchical, and procedurally parameterized), support interactive execution with full or partial observability, and provide rich, automated, and objective feedback (per-step, rubric-based, or via LLM/rule-based verifiers) (Huang et al., 12 Nov 2025).
• Benchmark Composition and Metrics: Metrics such as Success Rate (SR), Completion Ratio (CR), Execution Efficiency (EE), and catastrophic forgetting/adaptability indices provide detailed, progression-aware signals for agent assessment (Moulin et al., 2022, Xu et al., 1 Jul 2024), often in conjunction with cross-play and human–AI interaction evaluation (Jha et al., 17 Apr 2025).

4. Transfer, Retention, and Continual Adaptation

• Catastrophic Forgetting Mitigation: Ecosystem agent frameworks that never alter previously learned specialist agents ensure zero degradation of past skills (Moulin et al., 2022). Similarly, MAESTRO employs population replay buffers and joint curriculum to prevent regressive cycles in agent policy evolution (Samvelyan et al., 2023).
• Knowledge and Code Transfer: EnvBridge-style agent architectures explicitly record and retrieve successful code fragments; cross-environment adaptation is conducted via LLM-based transformation (“knowledge transfer” prompts) without gradient steps, achieving robust transfer in robotic manipulation benchmarks (Kagaya et al., 22 Oct 2024).
• Adaptive Selection of Learning Methods: Systematic analysis on AutoEnv-36 demonstrates that no single fixed learning strategy dominates across all environments; per-environment optimization method selection (“automatic meta-controllers”) achieves substantially higher normalized rewards but displays rapid diminishing returns as more candidate methods are added (Zhang et al., 24 Nov 2025).

5. Empirical Insights, Challenges, and Open Problems

Challenge	Observed Impact	Related Solutions
Policy overfitting to environment	Poor zero-shot generalization	Cross-task curricula, diversity metrics (Li et al., 2023)
Sequential skill attrition	Catastrophic forgetting	Ecosystem agents, buffer replay (Moulin et al., 2022)
Memory/computation scaling	Infeasible agent pools or code logs	Dynamic pruning, retrieval optimization (Moulin et al., 2022, Kagaya et al., 22 Oct 2024)
Mismatch of learning strategies	Diminished reward as env count increases	Meta-selection of optimization methods (Zhang et al., 24 Nov 2025)
Verification bottlenecks	Low-fidelity or costly evaluation	Automated, rubric-based, hybrid verifiers (Huang et al., 12 Nov 2025)

A recurring conclusion is that scalable cross-environment agent learning requires not only rich, diverse environment distributions and robust performance metrics, but also adaptive and possibly composable learning procedures. Adaptive selection and modular update frameworks provide partial mitigation, but there is no universal solution; automated meta-learning for “method selection” remains an important open direction.

6. Future Directions

• Automated Discovery and Combination of Learning Methods: Future benchmarks such as AutoEnv call for meta-controllers that invent, adapt, and fuse learning strategies dynamically as agents encounter new environments (Zhang et al., 24 Nov 2025).
• Scaling to Real and High-fidelity Environments: Further development of GEF-compliant environment suites, with improved sim2real transfer and robustified verifier pipelines, is needed for practical deployment (Huang et al., 12 Nov 2025).
• Structured Memory and Code Retrieval: Efficient, hierarchical or KNN-based memory retrieval for code/data/experience fragments will be essential as the scale of environments and agent interactions grows (Kagaya et al., 22 Oct 2024).
• Generalist Multimodal and Multi-agent Evolution: Integrating cross-platform and multimodal execution (as in CRAB), as well as open-ended, asynchronously evolving societies of agents (e.g., ARE), are recognized as necessary for progress toward generalist, lifelong agents (Xu et al., 1 Jul 2024, Huang et al., 12 Nov 2025).
• Meta-Evaluation and Robustness Metrics: Standardizing evaluation protocols, focusing on long-term skill retention, adaptation speed, transfer efficiency, and societal metrics (e.g., norm adoption, ad-hoc teamplay) will further mature the field (Jha et al., 17 Apr 2025, Samvelyan et al., 2023).

Cross-environment agent learning stands at the confluence of scalable environment design and meta-agent architectures. Contemporary results establish that static curricula or monolithic optimization pipelines do not suffice for robust generalization; only adaptive, modular, and meta-learned learning systems demonstrate persistent gains as environment distributions grow in heterogeneity and complexity.