Open-Ended RL & Simulation Sandboxes

• Open-ended RL and simulation sandboxes are dynamic environments with evolving task structures that promote continual learning and adaptability.
• These platforms employ modular, extensible simulation tools like MuJoCo Playground, MiniHack, and MineDojo to rigorously benchmark agent generalization and skill acquisition.
• The approach enables automated curriculum design and sim-to-real transfer by leveraging co-evolutionary interactions between agents and their environment.

Open-ended reinforcement learning (RL) and simulation sandboxes form the methodological and infrastructural basis for developing, evaluating, and benchmarking agents whose objectives, environments, or internal skills are not a priori fixed but co-evolve, diversify, or are self-generated throughout learning. These systems enable and challenge the development of generalist AI, testing capabilities in continual adaptation, skill acquisition, curriculum learning, and generalization well beyond single-task, static MDP settings. Open-ended sandboxes range from rich physics environments and video game engines, to multi-agent societal simulators and knowledge-integrated open worlds, with advances in both the simulation frameworks and the algorithmic paradigms that exploit them.

1. Defining Open-Ended RL and Simulation Sandboxes

Open-ended RL differs from traditional RL in that tasks, goals, environmental parameters, reward functions, or agent populations are not statically predefined but evolve, are procedurally generated, or autonomously constructed. An open-ended RL sandbox is thus a parameterized family of MDPs or POMDPs, in which agents face a continually renewed or expanding space of challenges.

Formally, in static sandboxes, the environment $\mathcal{E} = (S, A, T, R, O)$ defines fixed state/action spaces, transition dynamics, and rewards. In open-ended sandboxes, either the environment itself is a sequence $\{\mathcal{E}_t\}$ evolving over time, or the agent–environment system couples agent updates $$\theta \leftarrow \theta + \alpha\nabla_\theta J(\theta; \mathcal{E}_t)$$ and environment (or curriculum) updates $$\phi \leftarrow \phi + \beta\nabla_\phi U(\phi; \pi_\theta)$$, where $U$ encodes novelty, challenge, or diversity (Chen et al., 15 Oct 2025).

Key features include:

• Dynamic or procedurally generated tasks/goals
• Continual co-evolution of agent policy and environment distribution
• Intrinsically motivated or reward-free skill discovery
• Scalable and modular software allowing user- or agent-driven environment modifications

2. Simulation Platforms and Modular Architectures

Prominent open-ended RL sandboxes instantiate these abstractions through modular, extensible simulation infrastructure:

MuJoCo Playground (Zakka et al., 12 Feb 2025):

An open-source, GPU-native robot learning framework, MuJoCo Playground integrates the MJX physics engine (JAX-based, with GPU-resident, batched rigid-body simulation), Madrona batch renderer (CUDA-based ray-tracing and Vulkan rasterizer for high-throughput image observations), and Gym-compatible MDP interfaces. It supports parameterized environments across robot morphologies, domain-randomization distributions, and task curricula. Training loops are unrolled with vmap/JIT for tens of thousands of instances per device, yielding wall-clock convergence in minutes and validated on zero-shot sim-to-real transfer.

Kinetix, Jax2D (Matthews et al., 2024):

Establishes a procedurally parameterized family of 2D physics tasks using a JAX-accelerated Box2D-like engine, enabling billions of environment steps across heterogeneous scenes and morphologies. Kinetix defines task distributions $T \sim p(T)$, with transformer-based agents trained with learnability-based prioritized level replay.

NovelGym (Goel et al., 2024):

A gridworld-based platform with YAML/Python environment specifications, dynamic novelty injection, entity/action libraries, and PettingZoo multi-agent support. Its formalism allows for environment transformations (novelties) $\nu: E \rightarrow E'$, modular agent wrappers for hybrid planning and RL, and benchmarking routines for adaptation and generalization.

MiniHack and NLE (Samvelyan et al., 2021, Küttler et al., 2020):

MiniHack wraps NetHack’s procedural dungeon engine in a programmable Gym interface, supporting both a domain-specific description language (DSL) and a Python API for specifying complex grid-based RL benchmarks. NLE exposes the full stochasticity, partial observability, and compositional complexity of the NetHack universe, with tasks emphasizing exploration, skill acquisition, and transfer.

SDGym (Klu et al., 2023):

Bridges system dynamics modeling and RL by exposing user-specified SD models (XMILE/MDL) as Gym environments, enabling RL agents to optimize policies on complex, high-dimensional temporal systems.

OPEn (Gan et al., 2021):

An open-ended physics environment for representation learning, featuring procedurally generated 3D puzzles built atop ThreeDWorld, leveraging intrinsic exploration and unsupervised contrastive methods.

MineDojo (Fan et al., 2022):

A Minecraft-based, open-ended embodied agent benchmark exposing thousands of both programmatic and creative tasks, natural language conditioning, and large-scale data integration (video, wiki, forums). MineDojo leverages CLIP-based video–LLMs as learned reward functions, allowing for open-vocabulary instruction and evaluation.

Hyp-Mix (Mannekote et al., 2024):

A framework for LLM-simulated agent behavior in open-ended interactive learning environments, supporting modular composition of expert-defined behavioral hypotheses, LLM prompt calibration, and falsifiable evaluation against distributional predictions.

3. Algorithmic Paradigms for Open-Endedness

Sandboxes enable, and are informed by, algorithmic frameworks that assume environment, agent goal, or task distribution open-endedness.

Unsupervised Environment Design/UED (Jiang, 2023):

Models curriculum learning as a student–teacher minimax-regret game over parameterized MDP families $P(s_{t+1}|s_t,a_t;\theta)$, $r_t = \mathcal{R}(s_t,a_t,s_{t+1},\theta)$. UED algorithms generate autocurricula by having the teacher propose challenging environments at the frontier of the agent’s capability:

• PLR (Prioritized Level Replay): Maintains a buffer of high-value-loss tasks, adaptively samples and replays to encourage robustness.
• PAIRED: Three-player game combining protagonist, antagonist, and teacher to maximize regret on selected environment parameters.
• ACCEL: Edits high-regret levels to induce compounding complexity.
• SAMPLR: Corrects for covariate shift due to curriculum-induced parameter distributions.

These methods empirically improve zero-shot generalization and robustness.

Autotelic RL (Srivastava et al., 6 Feb 2025):

Eschews external task reward, formalizing skill acquisition as autonomous goal-generation and pursuit in reward-free MDPs. Agents sample or construct goals $g \in \mathcal{G}$, learn goal-conditioned policies $\pi(a|s,g)$, and receive intrinsic rewards, either knowledge-based (prediction error, information gain) or competence-based (progress in goal achievement). Taxonomies are provided for Intrinsically Motivated Goal Exploration Processes (IMGEPs), distinguished by goal representation, selection, and intrinsic reward signals.

Language–Policy Co-training (Zhai et al., 2023):

Embodied agents such as OpenPAL coordinate LLMs and RL controllers in bidirectional adaptation. LLMs are fine-tuned to translate natural-language instructions into discrete or structured goal forms; goal-conditioned RL policies learn to execute these; co-training aligns both, achieving open-ended generalization to unseen instructions and state combinations.

Agent–Environment Co-evolution and Societal Simulations (Chen et al., 15 Oct 2025):

In LLM-based multi-agent settings, open-endedness arises from dynamic scenario generation, memory-augmented generative agent architectures, and continual adaptation of both individual and societal-level state–action spaces. Research focus includes the stability–diversity trade-off, metrics for emergent innovation and resilience, and continuous co-evolutionary loops for scenario/agent development, exemplified in domains such as TwinMarket, AI-Economist, and AGA.

4. Evaluation Metrics, Benchmarks, and Generalization

Open-ended RL sandboxes demand metrics and protocols beyond fixed-task success:

• Diversity and exploration metrics: Fraction of goal space covered, mean state–feature distance, unique states discovered, entropy of visited state distributions (Srivastava et al., 6 Feb 2025, Gan et al., 2021, Chen et al., 15 Oct 2025).
• Generalization: Zero-shot test return on procedurally generated or human-designed holdout levels, generalization gap between train and test environments, and compositionality in referential games (Matthews et al., 2024, Srivastava et al., 6 Feb 2025, Gan et al., 2021).
• Robustness: Success rates under environmental perturbations, adaptation time post-novelty injection, stability under distributional shift (covariate correction, e.g., SAMPLR) (Jiang, 2023).
• Benchmarks: MiniHack’s room/corridor/skill suite, NLE’s procedural dungeons, Kinetix/Sandbox RL holdout tasks, OPEn downstream physical puzzles, MuJoCo Playground sim-to-real tasks, and MineDojo’s language-conditioned objectives.

Empirical evidence indicates that methods such as prioritized replay curricula, cross-task pretraining, and intrinsic motivation yield improved adaptation, sample efficiency, and broader generalization (Matthews et al., 2024, Jiang, 2023, Srivastava et al., 6 Feb 2025).

5. Implications and Open Problems in Open-Ended RL

Open-ended RL sandboxes have catalyzed advances in continuous skill acquisition, automated curriculum learning, robust sim-to-real transfer, and cross-domain generalization. Yet, they highlight unresolved challenges:

• Sample efficiency: Even best agents require millions of environment steps per downstream task (e.g., OPEn’s transfer gap (Gan et al., 2021)).
• Reward engineering: Intrinsic motivation and learned rewards (from video-LLMs, eg. MineCLIP) offer dense shaping but face scaling and domain adaptation issues (Srivastava et al., 6 Feb 2025, Fan et al., 2022).
• Expressiveness of goal/task spaces: Hand-specified, discrete subgoal spaces limit true open-endedness; generative models, language-grounded instructions, and symbolic abstractions are active research frontiers (Zhai et al., 2023, Chen et al., 15 Oct 2025).
• Multi-agent and societal complexity: Stability, innovation, and scalability remain in tension; new formal evaluation frameworks for emergent social behaviors, adaptability, and alignment are required (Chen et al., 15 Oct 2025).
• Integration with real world: Sim-to-real transfer depends on accurate domain randomization, sensor emulation, parameterization of aleatoric and epistemic uncertainties, and scaffolding of simulation curricula to mirror real-world diversity (Zakka et al., 12 Feb 2025, Jiang, 2023).

A plausible implication is that continued progress in open-ended RL will depend on ever-richer, more parameterized sandboxes, joint advances in agent architectures and simulation platforms, and principled, multi-faceted evaluation protocols.

6. Future Directions and Extensibility

Emerging extensions include:

• Incorporating hierarchical, symbolically abstracted goal spaces and planning modules for long-horizon tasks (MineDojo wiki guidance, GOFAI integration in NovelGym) (Fan et al., 2022, Goel et al., 2024).
• Scaling internet-scale data integration and retrieval-augmented reward models (MineDojo, Hyp-Mix) (Fan et al., 2022, Mannekote et al., 2024).
• Advancing continuous, co-evolutionary agent–environment loops for modeling complex societies and adaptive multi-agent systems (Chen et al. (Chen et al., 15 Oct 2025)).
• Domain generalization and sim-to-real via environment design automation, parameter ground-truthing, and adaptive domain randomization (MuJoCo Playground, UED sandboxes) (Zakka et al., 12 Feb 2025, Jiang, 2023).
• Formalization of compositional, multi-modal sensory inputs and action spaces.
• Community-contributed model exchanges, task parameter libraries, and open-access simulation suites (e.g., XMILE Exchange for SDGym, MiniHack’s DSL corpus, MineDojo’s multimodal base).

Efforts are converging on modular, extensible sandboxes, enabling rapid iteration from environment definition through to agent training, evaluation, and deployment in the open world.

7. Comparative Summary Table of Selected Open-Ended RL Sandboxes

Sandbox	Domain/Engine	Core Mechanisms
MuJoCo Playground	MJX + Madrona	GPU-accelerated physics/rendering, MDP factories, sim-to-real
Kinetix/Jax2D	Custom 2D physics (JAX)	Procedural task generator, transformer policy, prioritized curriculum
NovelGym	Gridworld/PettingZoo	YAML/API env spec, modular novelty injection, hybrid plan+RL
MiniHack/NLE	NetHack (C/C++)	DSL/Python API, procedural dungeons, multi-modal obs, UED support
SDGym	System Dynamics (PySD)	Stock-and-flow models as Gym envs, low-code interface
MineDojo	Minecraft + Internet	Open-ended tasks, video-language reward, knowledge integration
OPEn	ThreeDWorld (Unity)	Task-agnostic exploration, contrastive RL, intrinsic reward
Hyp-Mix	LLM (GPT-4 Turbo)	Hypothesis-based LLM simulation, modular composability

Each system occupies different portions of the open-ended RL design space, balancing scalability, domain realism, parameterization, and extensibility for foundational research and applied agent development.