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Agentic Environment Engineering for Large Language Models: A Survey of Environment Modeling, Synthesis, Evaluation, and Application

Jiachun Li, Zhuoran Jin, Tianyi Men, Yupu Hao, Kejian Zhu, Lingshuai Wang, Dongqi Huang, Longxiang Wang, Shengjia Hua, Lu Wang, Jinshan Gao, Hongbang Yuan, Ruilin Xu, Kang Liu, Jun Zhao

arXiv:2606.12191v1cs.CLcs.AI

TL;DR

Agentic-environment research lacks standardization and systematic analysis despite environments’ importance for developing LLM agents. This survey organizes environment engineering across modeling, synthesis, evaluation, and application, finding gaps in multi-agent suitability and experience management while identifying directions for scalable, reliable systems.

  • Problem

    Agentic environments lack standardized interfaces and reproducible deployment, limiting cross-environment generalization, unified benchmarking, and scalability.

  • Method

    The survey analyzes agentic environments across their engineering lifecycle, including attributes, synthesis paradigms, evaluation methods, and agent–environment co-evolution.

  • Results

    The survey finds existing environments inadequately support multi-agent settings and that synthesis is progressing toward more complex, realistic, and scalable environments.

  • Takeaways & Limitations

    Environment engineering should pursue standardized, scalable deployment alongside dynamic, multimodal, multi-agent, and neural-symbolic environments for more reliable and adaptive agent systems.

  • Takeaways & Limitations

    Experience-management mechanisms remain underdeveloped, with limited support for scalable insertion, deletion, updating, and retrieval.

Abstract

from arXiv · show

Environments serve as interactive systems for large language model (LLM) based agents across diverse scenarios and play a crucial role in driving the continual evolution of model capabilities. Despite this importance, existing work lacks a systematic categorization and deep analysis. This paper systematically studies current researches on agentic environments from the perspective of the environment engineering lifecycle, covering their modeling, synthesis, evaluation and application. Specifically, the paper first introduces representative environments from the perspectives of eight attributes and eight domains, providing detailed analyses of their development paths and highlighting their core capabilities. Second, for automated environment synthesis, two paradigms are introduced, such as symbolic synthesis and neural synthesis. This paper also shows different environment evaluation methods in each paradigm. Thirdly, the corresponding environment applications from the perspective of agent-environment co-evolution are discussed. In specific, the paper characterizes the primary pathways for agent evolution in dynamic environments from four complementary perspectives: memory-centric experience evolution, orchestration-centric workflow evolution, trajectory-centric offline evolution, and exploration-centric online evolution. And three paradigms of environment evolution are identified, namely neural-driven, difficulty-driven, and scaling-driven approaches. At last, several promising future directions are discussed, including Environment-as-a-Service, Multi-agent Environments, and Neural-Symbolic Environments.

1 INTRODUCTION

The survey frames agentic environments as interactive alternatives to costly, risky, privacy-sensitive, and irreproducible real-world interaction, then reviews their engineering lifecycle. It organizes the field around environment characteristics and domains, synthesis and evaluation, agent–environment co-evolution, and future deployment directions.

  • Motivation: Agentic environments simulate real-world scenarios for model interaction while avoiding real-world costs, safety risks, privacy concerns, and irreproducible failures.Manual simulation engineering remains resource-intensive and offers limited scenario coverage.
  • Survey scope: The survey addresses three questions concerning environment characteristics and categories, systematic construction and evaluation, and closed-loop agent–environment co-evolution.These questions structure the survey across Sections 3–7.
  • Environment landscape: Existing environments are analyzed across eight attributes and eight domains, with comparative analysis finding them inadequately suited for multi-agent settings.The domains are GUI, Deep Research, Embodied, Game, Tool, Code, Domain-Specific, and Cross-Domain.
  • Synthesis and evaluation: Environment synthesis is divided into symbolic and neural paradigms, while diversity, complexity, and fidelity remain under-researched evaluation dimensions beyond established correctness assessment.Symbolic synthesis uses verifiable rubrics; neural synthesis includes pixel-level, word-level, and latent-level modeling.
  • Agent–environment co-evolution: Agent evolution is categorized as memory-centric, orchestration-centric, trajectory-centric offline, or exploration-centric online evolution, while environment evolution is neural-driven, difficulty-driven, or scaling-driven.The categories respectively emphasize accumulated experience, workflow coordination, generated trajectories, reinforcement-learning exploration, parameter variation, curriculum complexity, and broader scenario coverage.
  • Future directions: Future directions include Environment-as-a-Service and environments that are dynamic, long-horizon, open-ended, multimodal, multi-agent, and neural-symbolic.Environment-as-a-Service is presented as a standardized, scalable, and reproducible deployment paradigm.

2 PRELIMINARIES

The paper formalizes environments as stochastic POMDPs and agents as history-conditioned policies that seek to maximize expected discounted return. For LLM agents, actions are primarily natural-language tokens, making interaction trajectories chains of thought and motivating Environment–Agent Alignment and agentic reinforcement learning.

  • Environment and agent formulation: Environments are modeled as stochastic dynamical systems represented by the POMDP tuple ⟨S, A, P, R, Ω, O, γ⟩.The components include latent states, available actions, probabilistic transitions, scalar rewards, observations, an observation function, and a discount factor γ ∈ [0,1).
  • Environment and agent formulation: Agentic environments support open-ended, language-centric, tool-augmented interactions by permitting LLM actions and returning observations and rewards.Actions may include natural-language responses or external-tool calls, while observations include code results or HTML pages and rewards include answer or format correctness.
  • Environment and agent formulation: Because the latent state is unobservable, the agent conditions its policy on history h_t, which maps to a probability distribution over actions.The history contains previous observations, actions, and rewards, and the policy selects actions probabilistically.
  • Environment and agent formulation: The agent optimizes a policy π* that maximizes expected discounted return J(π) over trajectories induced by transition and observation dynamics.For LLM agents, π corresponds to model parameters, while h_t records model–environment interaction history.
  • Environment–Agent Alignment: LLM actions primarily consist of generated natural-language tokens, so interaction trajectories largely form chains of thought.The survey calls optimizing π for expected returns Environment–Agent Alignment and contrasts teacher-generated offline SFT trajectories with emerging Agentic RL approaches.

LD-CLIP

The section contrasts static data engineering with environments that dynamically adapt task complexity, support interactive tool-mediated workflows, and couple agent actions to environmental feedback. It also defines a decoupled surrogate clipping objective and situates environments as necessary for continual agent improvement.

  • LD-CLIP: The decoupled surrogate clipping objective integrates lower and upper clipping bounds through the ratio r_i,t(θ) and advantage Â_i.The passage states that Â_i and r_i,t(θ) follow GRPO definitions, while DAPO’s refinements mitigate gradient vanishing and overshooting.
  • Environment versus traditional data: Traditional data engineering transfers information from a static corpus, creating a mismatch between fixed dataset difficulty and evolving model proficiency.Simple trajectories can cause diminishing returns because the dataset lacks complexity for further improvement.
  • Environment versus traditional data: Environments dynamically adjust data distributions and task complexity to match the agent’s growing capabilities.They act as generative engines that modulate task complexity based on real-time agent performance.
  • Interactive environments: Unlike single-turn datasets, environments support multi-turn interaction, external tools, and specialized roles that help agents decompose complex objectives into subtasks.Examples include code executors, knowledge retrievers, and other specialized agents.
  • Closed-loop learning: Environments form closed-loop control systems in which agent actions trigger state changes that provide feedback for learning task dynamics and recovery strategies.This coupling lets agents experience the outcomes of their own errors rather than relying only on static gold standards.

3 ENVIRONMENT ATTRIBUTE

The section organizes agent evaluation environments by multidimensional attributes that shape how agents perceive, interact, and decide. It distinguishes environments by transition dynamics, action adaptation, interaction mechanism, and state–observation access.

  • Overview: Environment attributes form a multidimensional taxonomy grounded in environments’ basic mechanics and mathematical formulations.These attributes determine how agents perceive, interact, and make decisions.
  • Transition Dynamics: Symbolic environments execute programmed logic, predefined rules, or physics engines, whereas neural environments use learned parameters and neural networks to approximate transitions.PDDL specifies symbolic transitions through logical preconditions and deterministic effects; DreamGen uses neural video world models to generate synthetic trajectories.
  • Action Adaptation: Open-loop agents execute action sequences predetermined from an initial observation, whereas closed-loop agents update actions using new observations and interaction history.Closed-loop systems can respond to stochasticity and correct deviations through reactive decisions.
  • Interaction Mechanism: Online environments evaluate agents through active sequential interaction and real-time feedback, whereas offline environments use static prerecorded trajectories without direct system interaction.Mind2Web and ALFRED compare predicted actions with human-annotated trajectories, while WebArena provides dynamically interactive websites.
  • Observability: Fully observable environments expose the complete state, with Ω= S and policies based on st, whereas partially observable environments provide incomplete or noisy observations requiring trajectory-level state inference.Fully observable settings are typically modeled as MDPs, while partially observable settings are modeled as POMDPs.
  • Outcome Certainty: Deterministic environments map each state-action pair to a predictable next state and reward, while nondeterministic environments permit randomness and uncertainty in outcomes.In deterministic settings, P(st+1∣st,at) = 1 for exactly one target state.

4 ENVIRONMENT DOMAIN

Agent evaluation environments span seven domains with distinct demands, from interface manipulation and evidence synthesis to embodied action and domain-specific reasoning. Their design is evolving toward executable, multimodal, and long-horizon settings that balance realism, diversity, controllability, and verifiability.

  • Environment Domains: The survey organizes environments into GUI, Deep Research, Embodied, Game, Tool, Code, and Domain-Specific domains, each imposing distinct demands on agents.These demands include grounded perception, interface manipulation, long-horizon planning, interactive decision making, evidence synthesis, tool use, and professional reasoning.
  • GUI: GUI environments test agents’ comprehension, localization, sequential decision making, and interaction with interface elements across desktop, mobile, and web platforms.Desktop tasks emphasize long-horizon planning and cross-application coordination; mobile tasks involve constrained screens and deeper page transitions; web tasks combine textual, visual, structured, and dynamic elements.
  • Deep Research: Deep Research environments evaluate information search, multi-source reasoning, and research report writing as agents retrieve, synthesize, and structure evidence.These settings test factual retrieval, broad evidence collection, cross-source comparison, conflict resolution, iterative exploration, synthesis, and verifiable report generation.
  • Embodied: Embodied environments place agents as robots or virtual characters in 3D spaces, requiring perception, movement, interaction, spatial navigation, physical manipulation, and long-horizon planning.Spatial navigation emphasizes spatial representations from egocentric observations and reliable action in unseen environments.
  • Environment Design Evolution: Environment design is shifting from static, narrow benchmarks toward executable, multimodal, and long-horizon settings.Future designs should balance realism, diversity, controllability, and verifiability to support robust training and reliable evaluation of general agents.

5 ENVIRONMENT SYNTHESIS

Because manual construction limits the scalability of environment quantity and diversity, recent research explores automated synthesis of large-scale, high-quality training environments. These methods are categorized by the resulting environment form into symbolic synthesis and neural synthesis.

  • Motivation: Manual construction limits the scalability of training-environment quantity and diversity, motivating automated approaches for synthesizing environments at scale.Large-scale, high-quality training environments are crucial for improving agent capabilities.
  • Synthesis Paradigms: Symbolic synthesis constructs environments with symbolic rules such as code, whereas neural synthesis represents environments with neural models such as world models.The categorization is based on the form of the resulting environments.

5.1 Symbolic Synthesis

Symbolic synthesis constructs interactive environments whose state transitions and feedback are explicitly controlled by symbolic rules or executable code. Its methods progress from task-driven and real-world-driven synthesis toward de novo generation, expanding environmental freedom and scale while raising challenges in correctness, diversity, and bias.

  • Foundations: Symbolic environments explicitly control state transitions and feedback through symbolic rules or executable code, enabling reliable feedback and diverse, high-quality training environments.They are formally represented as E = ⟨S, A, P, R⟩, with P mapping states and actions to states.
  • Task-driven synthesis: Task-driven synthesis wraps static tasks and data, including tool-calling and mathematical data, with programmatic rules to create scalable interactive environments.The approach leverages abundant high-quality static data to support correctness and scalability.
  • Task-driven synthesis: Task-driven methods span coding, tool usage, and knowledge-intensive domains, with systems scaling environments through multi-agent synthesis, heterogeneous APIs, state dependencies, and execution-grounded verification.Examples include Scale-SWE, AgentScaler, EnvScaler, MedAgentGym, SciAgentGym, and PaperArena.
  • Real-world-driven synthesis: Real-world-driven synthesis projects complex Web, Game, GUI, operating-system, and software-tool interactions into simplified virtual environments, achieving greater scalability than task-driven approaches.These environments support massive-scale training, long-horizon trajectories, visual feedback, and physical constraints.
  • De novo synthesis: De novo synthesis generates interactive environments from minimal examples or zero-shot prompts, maximizing freedom and environmental space while requiring mechanisms for internal logic, code correctness, task diversity, and quality stability.Approaches include state-structure and logic-graph generation, forward deductive synthesis, procedural testing, graph expansion, execution-level self-correction, and automated interface encapsulation.

5.2 Neural Synthesis

Neural synthesis models environments with neural networks, typically world models that learn state transitions and observations for agent interaction in learned environments. It comprises pixel-level, word-level, and latent-level modeling, trading off fidelity, abstraction, compactness, interpretability, and computational efficiency.

  • Neural synthesis uses world models to learn environment state transitions and observations, enabling agents to interact with learned rather than real environments.
  • Neural environment modeling divides into pixel-level, word-level, and latent-level paradigms based on their environment-state representations.
  • Pixel-Level Modeling: Pixel-level modeling preserves rich physical and scene information from high-dimensional visual signals, while supporting increasingly interactive and long-horizon environments.
  • Word-Level Modeling: Word-level modeling represents environments in natural language, reducing computational cost and supporting complex reasoning and long-horizon planning across diverse domains.
  • Latent-Level Modeling: Latent-level modeling predicts dynamics in learned representation spaces, with pretrained DINOv2 representations enabling cross-domain generalization and efficient forecasting from offline data.
  • Pros and Cons; Future Directions: The paradigms trade off fidelity, abstraction, compactness, predictive capability, interpretability, and information redundancy, while long-horizon consistency, essential-information preservation, efficiency, and controllability remain challenges.

5.3 Quality Control and Evaluation of Environments

Environment quality is evaluated across correctness, diversity, complexity, and fidelity, assessing whether synthesized environments are reliable substrates for agent training and evaluation. Existing work is shifting from post-generation filtering toward closed-loop generation with continuous validation and refinement.

  • Evaluation dimensions: Quality control evaluates synthesized environments along four dimensions: correctness, diversity, complexity, and fidelity.These dimensions assess whether environments can reliably support agent training and evaluation.
  • Correctness: Correctness requires valid state transitions, legally executable tasks, valid solutions, and reliable verifiers that provide correct reward signals.Symbolic environments commonly use program execution, sandboxes, unit tests, golden tool sequences, trajectory validation, and execution-based evaluators.
  • Diversity: Diversity measures broad, non-redundant coverage of task, state, tool, and linguistic spaces to reduce surface-pattern overfitting.Methods include embedding-based deduplication, clustering, structured coverage of tools and APIs, and diverse neural outputs under identical inputs.
  • Complexity: Complexity evaluation seeks solvable task distributions with appropriate difficulty rather than simply increasing task length.Symbolic methods use structural parameters or human and strong-model performance, while neural methods adjust planning depth, branching factor, or candidate-plan counts.
  • Quality-control direction: Environment synthesis is moving from post-generation filtering toward closed-loop generation with continuous validation and refinement.This direction targets quality-controlled environment synthesis.

6 AGENT EVOLUTION

Agent evolution advances an agent’s capabilities through mechanisms typically realized by continuous interaction with the external environment. It includes both external structural adaptations and internal parametric changes, including memory-centric experience evolution.

  • Agent Evolution: Agent evolution advances an agent’s capabilities through continuous interaction with the external environment.The process is defined as capability advancement through various mechanisms.
  • Agent Evolution: Agent evolution encompasses external structural adaptations and internal parametric changes.These represent the two broad forms of change described for evolving agents.
  • Agent Evolution: Memory-Centric Experience Evolution enhances task-processing capabilities by accumulating and leveraging experience from the external environment.It is identified as one form of external structural adaptation.

6.1 Memory-Centric Experience Evolution

Memory-centric experience evolution stores and retrieves trajectories, procedural knowledge, and other experiences to improve task handling, maintain long-term memory, and support capability co-evolution. The section distinguishes instance trajectories from abstract scripts, emphasizes skills and experience maintenance, and identifies systematic experience management as an open direction.

  • 6.1 Memory-Centric Experience Evolution: External memory bases store and retrieve trajectories, experiences, or process knowledge to improve problem solving, long-term memory, and cross-task capability evolution.Experience can be represented and utilized at different granularities and through diverse strategies.
  • 6.1 Memory-Centric Experience Evolution: Instance trajectory experience records complete task interactions and provides detailed decision information, but its context dependence limits generalization.It captures correct or incorrect actions and other agent operations in specific task contexts.
  • 6.1 Memory-Centric Experience Evolution: Abstract scripts generalize across multiple trajectories to extract reusable task patterns, while structured memory categories and tool-graph integration support personalized interactions and procedural execution.Examples include persona, working, and episodic memory, plus procedural summaries represented as graph edges.
  • 6.1 Memory-Centric Experience Evolution: Agents evolve experiences through reflection on successful and failed actions, structured repository maintenance, and skill synthesis or reuse in libraries and hierarchical skill banks.Agent-Pro corrects wrong steps through iterative reflection, while SAGE and SkillRL organize and reuse procedural skills.
  • 6.1 Memory-Centric Experience Evolution: Experience granularity and utilization strategy are central concerns, with skills receiving increasing attention as structured, reusable experience.The section treats both the scale and effectiveness of experience as important.
  • 6.1 Memory-Centric Experience Evolution: Future systems should aggregate large-scale human and open-source experiences while supporting principled insertion, deletion, updating, and retrieval operations.Current experience management mechanisms provide limited support for comprehensive lifecycle operations.

6.2 Orchestration-Centric Workflow Evolution

Orchestration-centric workflow evolution spans fixed, automated, and persistently evolving structures with increasing structural autonomy. Automated workflows adapt coordination through an orchestrator, while evolving workflows modify topology and capabilities over continued environmental interaction.

  • Fixed Workflow: Fixed workflows use developer-defined deterministic topologies with sequential logic, conditional branches, local loops, and prespecified retries.Agents perform specific atomic operations without authority over the global workflow structure.
  • Automated Workflow: Automated workflows use an orchestrator to construct or adjust workflows, decompose objectives, coordinate workers, and adapt execution paths using real-time feedback.They decouple high-level planning from low-level execution to adapt to dynamic environments.
  • Evolving Workflow: Evolving workflows persistently modify task topology as tasks accumulate and can introduce and retain new tools or roles during runtime.This distinguishes structural evolution from merely changing states across tasks.
  • Comparative Analysis: Training a central orchestrator offers Automated Workflow a more direct path to improving collective system performance than training static, decentralized roles in Fixed Workflow.The comparison concerns capability enhancement through workflow-level coordination rather than model training alone.
  • Future Directions: Collective evolution in Evolving Workflow can help entire systems adapt to increasingly complex and highly variable environments beyond isolated agent training.The approach targets complex environments where fragmented node-level training may not meet practical demands.

6.3 Trajectory-Centric Offline Evolution

Trajectory-centric offline evolution enhances agents through synthesized interaction trajectories and follows a three-stage process: task synthesis, trajectory synthesis, and trajectory refinement. Existing methods organize task construction into three paradigms, trajectory generation into four approaches, and refinement into filtering, correction, and iterative refinement.

  • Process: Trajectory synthesis comprises task synthesis, trajectory synthesis, and trajectory refinement before trajectories are used for training.Task synthesis determines the tasks, trajectory synthesis converts them into interactions, and trajectory refinement improves their quality and reliability.
  • Task Synthesis: Task construction follows Resource Transformation, Reverse Synthesis, and Structure-based Synthesis paradigms.These paradigms respectively transform prior resources, derive tasks from environmental exploration, or use explicit structures such as graphs, trees, schemas, and ASTs.
  • Trajectory Synthesis: Trajectory generation mainly uses Trajectory Augmentation, Sequential Interaction, Tree Search, and Model Simulation.Tree-search methods can identify higher-quality paths with DFS or MCTS, while simulation methods use components such as users, tools, and world models.
  • Trajectory Refinement: Trajectory refinement selects training data through Filtering, Correction, and Iterative Refinement.Filtering removes low-quality samples, correction repairs erroneous trajectories, and iterative refinement connects generation, verification, correction, and retraining in a closed loop.
  • Comparative Analysis and Future Directions: Research is shifting toward more complex, realistic tasks and trajectories that capture planning, reflection, and recovery while preserving verifiable quality boundaries during scaling.Future priorities include aligning trajectories with training objectives, verifying long-horizon data at low cost, stabilizing failure and recovery as training assets, and preventing self-reinforced biases.

6.4 Exploration-Centric Online Evolution

Exploration-centric online evolution uses reinforcement learning to improve agent capabilities, reduce forgetting, and target task-specific behaviors through reasoning, reward, and training-design innovations. Research increasingly emphasizes stable, efficient exploration and scalable algorithms, alongside integrating experience and skills into deployable training systems.

  • Reinforcement Learning: Reinforcement learning improves agent capabilities, mitigates catastrophic forgetting, and targets specific tasks or behaviors through reasoning, reward, and training-algorithm changes.It is presented as a crucial method for agent evolution and can improve performance across multiple tasks.
  • Inference Structure Design: Specialized inference structures organize complex-task reasoning through explicit process tags, evidence refinement, multimodal tokens, external memory, and world-model simulation.Examples include Search-R1, AutoRefine, Video-Thinker, M3-Agent, and ITP.
  • Reward Design: Reward design has evolved from outcome correctness toward multidimensional, task-specific signals covering efficiency, alignment, stability, and process supervision.Methods penalize redundant searches, constrain retrieval or memory sparsity, combine answer and format rewards, and provide dense visual milestone signals.
  • Reinforcement Learning Optimization: Training innovations improve credit allocation, exploration, sampling, and stability through advantage estimators, entropy balancing, variance-aware trajectories, structured sampling, and alternating RL-supervised updates.These methods address stochasticity, sparse rewards, overfitting, gradient efficiency, communication, and entropy collapse in online reinforcement learning.
  • Comparative Analysis and Future Directions: Research prioritizes efficient reasoning, fine-grained rewards, stable training, effective exploration, and especially scalable training-algorithm optimization, while future work targets adaptive agents and reliable real-world deployment.Proposed extensions incorporate experience, skills, and personalization into training and develop general-purpose algorithms bridging research and practice.

7 ENVIRONMENT EVOLUTION

Environment evolution is organized into three paradigms: neural-driven evolution learns and optimizes environment models, difficulty-driven evolution adapts task difficulty, and scaling-driven evolution broadens environment diversity and structure. Together, these paradigms treat environment evolution as a core component of continual agent capability improvement.

  • Neural-Driven Evolution: Neural-driven evolution represents the environment as a learned model optimized to provide adaptive interactions or simulated transitions during agent learning.It includes self-play environments and separately learned world models that approximate environment dynamics.
  • Neural-Driven Evolution: Self-play makes environment evolution endogenous by having agents or role-specialized variants generate training tasks while also learning from them.Absolute zero uses one unified model as both proposer and solver, generating tasks to maximize its learning progress.
  • Difficulty-Driven Evolution: Difficulty-driven evolution uses curriculum learning to adjust environments toward agent capabilities through explicit signals or implicit mechanisms.Explicit signals include accuracy, regret, reward, and curiosity, whereas implicit curricula emerge from adaptive task generation, environment construction, or manual design.
  • Difficulty-Driven Evolution: Implicit curricula can progressively increase task complexity through environment-agent co-evolution or adaptive task generation without an explicit difficulty controller.POET evolves environments and agents through mutation, filtering, and transfer, while DreamGym prioritizes tasks with high reward entropy.
  • Scaling-Driven Evolution: Scaling-driven evolution expands the environment distribution by increasing scenario diversity within environment families or coverage of different domains and structures.Scenario-level scaling increases the number and variety of tasks, trajectories, and interactions within a shared environment paradigm.

8 CHALLENGES & FUTURE DIRECTIONS

The section identifies interface standardization, realism, multi-agent complexity, and limited scientific grounding as major challenges for agentic environments. It proposes EaaS, co-evolution, environment scaling laws, and environment–capability mapping as promising directions.

  • Environment-as-a-Service: Environment heterogeneity in observations, actions, rewards, and interaction paradigms limits cross-environment generalization, unified benchmarking, and reproducibility.Environment-as-a-Service standardizes interfaces through cloud-hosted environments encapsulated behind unified APIs.
  • Realistic and Multi-agent Environments: Static, short-horizon, closed-world, single-modality environments fail to represent dynamic real-world settings that require timely decisions.Real-world states may change during agent interaction, increasing the need for responsiveness as well as accuracy.
  • Realistic and Multi-agent Environments: Multi-agent environments introduce non-stationarity, inter-agent dependencies, uncertain decision spaces, difficult credit assignment, and less stable reinforcement learning.Each agent’s behavior influences others, requiring continuous strategy adaptation.
  • Neural-Symbolic and Realistic Environments: Synthetic environments retain a substantial sim-to-real gap because factual errors, invalid transitions, incorrect feedback, and simplified approximations can produce spurious policies.The gap reflects the complexity, dynamics, and openness of real-world environments.
  • Agent–Environment Co-evolution: Environment engineering should move from fixed passive systems toward bidirectional agent–environment co-evolution, with environments dynamically adjusting task difficulty in response to agents.This shift extends beyond adapting agents to environments by requiring environments themselves to evolve.
  • Scientific Foundations: Future research should establish environment scaling laws and environment learnability, and map environment properties to the capabilities they cultivate for targeted construction.Relevant scaling factors include environment number, diversity, interaction horizon, and complexity.
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