Gym4ReaL: Real-World Gym Benchmarking

• Gym4ReaL is a suite of technological frameworks and benchmark environments designed to simulate real-world gym scenarios and reinforcement learning challenges.
• It integrates applications such as automated exercise detection, IoT-based gym infrastructures, and wearable sensor algorithms across various domains including water management and finance.
• Researchers benefit from a standardized, open-source platform that tests RL algorithms in dynamic, partially observable, and multi-objective settings, driving practical, robust solutions.

Gym4ReaL encompasses a diverse array of technological frameworks, benchmarking suites, and hardware/software platforms unified by the goal of real-world, robust recognition, tracking, and evaluation of gym exercises. Its manifestations include systems for automated fitness event detection, IoT-based gym infrastructure, advanced wearable sensor algorithms, and—most notably—a recent open-source suite for realistic reinforcement learning (RL) benchmarking.

1. Conceptual Origins and Motivations

Gym4ReaL arose from challenges in both applied fitness technology and reinforcement learning research. In fitness applications, problems include accurate exercise recognition, reliable repetition counting, and form assessment using non-invasive technology across variable conditions (lighting, user variation, occlusion). In RL research, the domain addresses the "reality gap" between simulated environments and complex, dynamic, and partially observed real-world processes, highlighting the necessity for communities to embrace realistic benchmarks that facilitate algorithmic transfer and operational deployment.

The designation Gym4ReaL, as formalized in the recent benchmarking initiative (2507.00257), specifically references an RL environment suite designed to expose RL algorithms to realistic domain properties—non-stationarity, partial observability, multi-objective trade-offs, risk, and data scarcity—rarely present in classic RL testbeds.

2. Suite Structure and Domain Coverage

The Gym4ReaL RL benchmarking suite includes six heterogeneous environments, each modeling distinct real-world decision-making challenges:

Environment	Domain	Real-world Features Modeled
DamEnv	Water/energy	Dam management with flood/starvation risk, realistic supply/demand
ElevatorEnv	Logistics	Elevator dispatch, passenger arrivals, waiting queues
MicrogridEnv	Energy	Battery storage, market trading, wear/battery degradation
RoboFeederEnv	Robotics	Industrial picking/planning with visual input, real robot constraints
TradingEnv	Finance	High-frequency trading, partial observability, transaction costs
WDSEnv	Water	Municipal water distribution, resilience, pump/tank control

All environments feature configurable parameters, compatibility with Gymnasium interfaces, and support both continuous and discrete state/action spaces. Many environments integrate real data (e.g., historical market, demand, or physical control logs), further enhancing realism.

3. Real-World Complexity and Algorithmic Challenge

Gym4ReaL environments depart from standard benchmark conventions by explicitly modeling:

• Partial Observability: Agents must act without access to the full system state, as in real-world trading and energy management tasks.
• Non-Stationarity: Market regimes, user demand, and system behavior change over time, invalidating stationarity assumptions.
• Limited Data and Operations Risk: No infinite simulation; operational mistakes may incur real penalties or be unsafe.
• Large State-Action Spaces: Control/tuning problems, e.g., microgrids or robotics, require high-dimensional, continuous input and control.
• Multi-objective Trade-offs: Most tasks feature conflicting objectives (e.g., profit vs. equipment lifespan; service delay vs. energy cost).
• Safety and Physical Constraints: Actions must respect feasibility regions—e.g., dam overflow, battery limits, elevator loads.

The environments are annotated for their suitability for advanced RL research paradigms, including risk-sensitive RL, hierarchical RL, imitation learning, and multi-objective optimization.

4. Experimental Methodology and Key Results

In each environment, RL algorithms such as PPO, DQN, Q-learning, and SARSA are evaluated against domain-relevant rule-based or expert heuristics. Training is conducted using real or plausible datasets, with multi-seed replication for stability.

Selected findings:

• In DamEnv, PPO outperforms heuristic water release policies in both average returns and overflow mitigation, as measured by aggregated reward:

$$\max \sum_{t=1}^T [r_{d}(a_t) + r_{\mathrm{of}}(a_t) + r_{\mathrm{st}}(a_t)]$$

• ElevatorEnv demonstrates that RL methods reduce passenger waiting times beyond simple dispatch heuristics.
• In MicrogridEnv, PPO leads to higher energy profit than "battery-first" or "only-market" baselines, though with observed policy variance—indicative of non-trivial generalization difficulties.
• RoboFeederEnv supports both planning (job order selection) and picking (vision-based grasping), with PPO learning robust strategies superior to static heuristics.
• TradingEnv sees RL methods reducing profit-and-loss variance (i.e., risk), but not always outperforming "buy-and-hold" baselines on return, illustrating risk-performance tradeoffs.
• WDSEnv shows that DQN-based controllers better maintain system resilience compared to default or random pump schedules.

All experiments provide complete code, documented environment configuration, and exhaustive result visualizations (curves, boxplots) to facilitate reproducibility.

5. Technical Features and Extensibility

The suite is fully open-source with standardized environments, fully parameterized interfaces, and explicit support for multi-objective tasks, visual input (notably in RoboFeederEnv), and fast adaptation to new domains. Environment architectures are designed for ease of community extension: new environments, stochastic wrappers, or data-driven variants can be contributed to expand coverage.

Additional technical elements, as detailed in the suite:

• Hyperparameters and experimental scripts published for repeatability.
• Environments labeled for suitability to various RL paradigms (e.g., risk, hierarchy, imitation).
• For data-intensive settings (e.g., WDSEnv), plans exist for optimized simulator wrappers.

6. Implications and Impact

Gym4ReaL provides RL researchers and practitioners with substantially more realistic evaluation settings, offering critical insight into the generalization, robustness, and operational safety of RL algorithms. By confronting algorithms with real-world complexities—such as partial information, dynamics shifts, competing objectives, and operational uncertainty—the suite is positioned to drive the next generation of RL research towards practical deployment.

Potential benefits include:

• Improved translation of research advances into industrial and public sector applications.
• Benchmarking for robustness and adaptive capacity, not just headline performance.
• Fostering cross-domain collaboration and standardization in real-world RL evaluation.

7. Community Directions and Ongoing Development

The developers advocate for a collaborative, community-driven extension model: contributions of new environments, expanded datasets, and improved simulators are solicited. Planned enhancements include lightweight simulators for resource-constrained research, standardized public datasets for head-to-head benchmarking, and additional environments covering further real-world domains or RL paradigms (e.g., safe RL, transfer learning, offline RL).

A plausible implication is that Gym4ReaL may become a reference platform for testing and developing RL algorithms intended for real-world deployment, setting a higher standard for empirical evidence of robustness and utility. Advancing this direction requires active maintenance, cross-disciplinary input, and continued alignment with user and industry needs.

Summary Table: Environments and Core Properties

Environment	Domain	Challenges Modeled
DamEnv	Water/Energy	Partial obs., non-stationary, safety
ElevatorEnv	Logistics	Queueing, discrete/continuous state, risk
MicrogridEnv	Energy	Multi-objective, market uncertainty
RoboFeederEnv	Robotics	Visual input, hierarchy, real robot
TradingEnv	Finance	Real data, partial obs., cost/risk
WDSEnv	Water	Multi-objective, resilience, constraint

Conclusion

Gym4ReaL designates a growing ecosystem of tools and environments for real-world gym and RL research, with the 2025 benchmarking suite at its forefront. Through its diversity, realism, and openness, the suite establishes a critical infrastructure for advancing RL from controlled simulation toward practical, generalizable application.