SimGym: Modular Simulation & RL Integration

• SimGym is a modular simulation framework that decouples domain simulators from RL interfaces through standardized Gym-style APIs.
• It supports plug-in extensions and heterogeneous simulations in domains such as robotics, e-commerce, and energy optimization.
• SimGym promotes reproducibility, benchmarking, and rapid prototyping by unifying simulation workflows with learning-based decision making.

SimGym is a technical term used in the simulation, reinforcement learning, and agent-based modeling communities to denote a class of frameworks or workflows that expose domain-specific simulators through OpenAI Gym-style (or Gymnasium-style) APIs, thus enabling seamless integration with learning-based agents and standard RL toolkits. Originally popularized to describe decoupled architectures for simulation and control (Schuderer et al., 2021), the term has gained traction with the emergence of large-scale toolkits in domains such as e-commerce (Castelo et al., 1 Feb 2026), aerial robotics (Kulkarni et al., 3 Mar 2025), building energy optimization (Campoy-Nieves et al., 2024), and flight systems (Wood et al., 2020). SimGym frameworks define programmatic interfaces and dataflows to unify the workflow of transforming arbitrary simulators—ranging from agent-based economic models to multirotor physics engines—into RL-compatible environments, support plug-in extensions, and facilitate reproducibility and benchmarking.

1. Conceptual Foundations of SimGym Architectures

SimGym, as a class, is characterized by the decoupling of domain simulation logic from reinforcement learning (RL) environment interfaces, enabling researchers to maintain simulation back-ends independently of RL-specific endpoint logic (Schuderer et al., 2021). The canonical architecture consists of:

• Domain Model: A fully versioned, domain-specific simulator (e.g., agent-based finance system, EnergyPlus building model, multirotor dynamics, browser interaction emulator).
• Simulation Interface Layer: A mediation class or protocol that implements the main simulation loop, formalizes entry points (reset, run, stop), and coordinates synchronization between domain simulation and RL agent.
• Decision Points as Environment Endpoints: Decorators or registration functions (e.g., @make_step) expose state-action-reward endpoints, specifying observation/action spaces and reward mappings.
• Gym Environment Wrapper: A wrapper that implements the OpenAI Gym or Gymnasium API, thereby exposing step(), reset(), action_space, and observation_space.
• RL Agent Integration: Policies or agents interact with the environment using standard RL workflows.

Table: Core Abstractions in SimGym-Style Frameworks

Layer	Principal Role	Example Implementation
Domain Model	Physics/logic definition	EnergyPlus, JSBSim, ABM code
Simulation Interface	Mediate sim ↔ RL, schedule steps	SimulationInterface subclass
Endpoint Registration	Mark agent decision loci	@make_step, task decorators
Environment API	Expose Gym step/reset, spaces	gym.Env, Gymnasium API
Plug-in/Extension System	Hot-swap reward, logger, sensors	Plugin hooks, method attach

This architecture promotes modularity, rapid iteration, and the ability to swap agents or evaluation protocols without recompilation or source modification.

2. Domain-Specific Implementations

The principles of SimGym are instantiated across diverse domains:

• Agent-Based Economic Simulation: In Sim-Env (Schuderer et al., 2021), decision points (e.g., trading, resource allocation) become gym.Env endpoints; plugin hooks enable reward, observation, or physics overrides without altering source simulation code.
• E-Commerce Browser Agents: In SimGym (2026), synthetic buyers powered by LLMs interact with live storefronts in real browsers, driven by traffic-grounded persona extraction pipelines. Each synthetic agent acts in a perceive–plan–act loop with schema-constrained actions and prompt-composed objectives, mounting “offline A/B tests” against production UIs (Castelo et al., 1 Feb 2026).
• Aerial Robotics: Aerial Gym Simulator exposes GPU-parallelized Isaac Gym physics paired with per-step geometric and neural controllers, ray-tracing pipelines for sensor simulation, and RL-compatible APIs supporting rigid-body dynamics, sensor noise, and sim-to-real transfer (Kulkarni et al., 3 Mar 2025).
• Building Energy Optimization: Sinergym wraps EnergyPlus with a Gymnasium interface, supporting dynamic observation/action spaces, stochastic weather, plug-in reward classes, and compatibility with RL baselines (Campoy-Nieves et al., 2024).
• Flight Control: GymFG exposes JSBSim/FlightGear-based flight dynamics models with multi-agent/competitive team abstractions, supporting distributed simulation and imitation learning (Wood et al., 2020).

3. Formal MDP Abstraction and Environment Specification

All SimGym-style frameworks cast the environment-agent interaction as a Markov Decision Process (MDP) defined by $(S, A, T, R, \gamma)$:

• $S$: State space, which corresponds to structured simulation variables or extracted observations;
• $A$: Action space, defined at decision points via decorators or task-specific schemas (e.g., setpoints, control vectors, browser actions);
• $T: S \times A \to S$: Transition map implemented by the simulator’s next-state logic;
• $R: S \times A \to \mathbb{R}$: Scalar reward map, often pluggable at runtime or via plugin hooks;
• $\gamma$: Discount factor.

For example, in building control via Sinergym, $r_t$ penalizes energy and comfort violations, with reward mappings either linear in HVAC power and comfort gaps or exponential in discomfort components (Campoy-Nieves et al., 2024). In e-commerce browsing, per-agent add-to-cart events act as binary episodic rewards; aggregate conversion rate differences $\Delta CR_{agent}$ serve as the offline analog to experimental lift (Castelo et al., 1 Feb 2026).

4. Extension Mechanisms, Plug-in Architecture, and Customization

A distinguishing feature of SimGym environments is runtime extensibility via platform-specific plug-in systems:

• Reward and Observation Swapping: Plugin hooks such as expose_to_plugins and attach_handler enable researchers to override or interpose logic for simulation steps, reward computation, and state extraction at runtime, supporting experiment reproducibility and metric ablation (Schuderer et al., 2021, Campoy-Nieves et al., 2024).
• Sensor and Controller Insertion: In aerial robotics, custom controllers (e.g., geometric, PID, neural) and sensors (e.g., proximity, ray-casting) are registered via APIs supporting JIT PyTorch modules, with runtime selection or replacement possible (Kulkarni et al., 3 Mar 2025).
• Flexible Agency: Any simuland or entity in the domain model can become an agent endpoint, not constrained by simulation inheritance hierarchies, facilitating multi-agent extensions (albeit with some frameworks lacking native PettingZoo-style support (Schuderer et al., 2021)).

These extension mechanisms enable rapid iteration for reward engineering, ablation studies, and experiment tracking without modifying core simulation code.

5. Benchmarking, Performance, and Evaluation

SimGym environments prioritize both fidelity and efficiency:

• Overhead Minimization: Threading/continuation overheads are usually on the order of microseconds to milliseconds per step, negligible for computationally demanding simulations (Schuderer et al., 2021, Kulkarni et al., 3 Mar 2025).
• Parallelization: GPU-accelerated implementations (Aerial Gym) achieve up to $4.43 \times 10^6$ simulation steps per second for batched quadrotor environments. Rendering rates for depth and segmentation sensors scale linearly with the number of parallel instances (Kulkarni et al., 3 Mar 2025).
• Reproducibility and Monitoring: Logging wrappers, run folders, and Docker images ensure reproducible experimentation. Performance profiles (e.g., accuracy, convergence, energy savings, sim-to-real error) are logged per episode (Campoy-Nieves et al., 2024, Kulkarni et al., 3 Mar 2025).
• Alignment Metrics: In e-commerce SimGym, the quality of agent-based predictions is quantified by sign alignment rate $A$ (directional agreement with human outcomes), Pearson correlation $\rho$ between agent and human conversion shifts, ablation studies for policy memory/persona impact, and sample-complexity curves (Castelo et al., 1 Feb 2026).

Table: Representative Performance Metrics

Framework	Throughput	Key Metric	Value / Characteristic
Aerial Gym	$4.43 \times 10^6$ SPS	Sim2real position error	$0.09$–$0.1$ m
Sinergym	$<$1 ms/step	Energy reduction (PPO)	$15$–$18$\% improvement
E-Commerce SimGym	$<$1 h/experiment	Sign alignment $A$	$69$\%

6. Limitations and Future Directions

Currently reported limitations include:

• Multi-Agent Scaling: Not all frameworks provide native support for vectorized or multi-agent environments (notably, lack of goal-conditioned/ PettingZoo APIs in some (Schuderer et al., 2021)).
• Coupling Mechanisms: Many systems rely on thread-based continuations rather than true language-level coroutines or async primitives.
• Domain Coverage: Some SimGym frameworks do not yet capture purely visual changes (e.g., lacking vision-enabled agents in e-commerce offline evaluation (Castelo et al., 1 Feb 2026)).
• Personalization and Learning: Agent personas are derived from engineered features and post-processed LLM prompts, with end-to-end persona learning from raw trajectories identified as an open area (Castelo et al., 1 Feb 2026).
• Sim2Real Transfer: Despite demonstrated sim2real pipeline fidelity in aerial robotics, transfer performance is task and domain dependent and subject to model discrepancy (Kulkarni et al., 3 Mar 2025).

Proposed future work includes PettingZoo/GoalEnv compliance, async coroutine integration, expansion of benchmark domains (Fin-Base, new building archetypes), and tighter coupling between offline simulation and automatic design optimization loops (Schuderer et al., 2021, Campoy-Nieves et al., 2024, Castelo et al., 1 Feb 2026).

7. Significance and Research Context

SimGym environments represent the evolution of RL simulation frameworks from tightly coupled, monolithic toolkits to modular, extensible, and domain-agnostic interfaces. This abstraction facilitates rigorous benchmarking, multi-domain evaluation, and end-to-end reproducibility. By marrying high-fidelity simulation, extensible plugin architectures, and standard Gym APIs, SimGym frameworks accelerate the development and deployment of intelligent control and data-driven decision making across scientific, industrial, and commercial settings (Schuderer et al., 2021, Kulkarni et al., 3 Mar 2025, Campoy-Nieves et al., 2024, Wood et al., 2020, Castelo et al., 1 Feb 2026).