gr-libs: Modular Framework for Goal Recognition

• gr-libs is a modular, open-source framework that standardizes goal recognition research by decoupling algorithmic components, benchmarks, and diagnostics.
• It integrates diverse algorithmic implementations, including MDP-based and metric-learning methods, to evaluate goals from partial observations.
• Its compatibility with Gymnasium environments and extensible diagnostic tools facilitates reproducible, controlled comparisons across dynamic goal recognition methods.

gr-libs refers to a modular, open-source software framework supporting research, development, and standardized benchmarking of Goal Recognition (GR) algorithms, especially in online and dynamic settings. It targets the disambiguation of agent goals from partial behavioral observations in environments modeled using Gymnasium standards, and is tightly coupled with the gr-envs package, which supplies compatible environments for systematic comparison and assessment of GR methods (Matan et al., 27 Sep 2025).

1. Architecture and Design Principles

gr-libs is architected to decouple algorithmic components, diagnostic modules, and benchmark specifications. Its primary objective is standardization—providing a reproducible platform where disparate GR algorithms can be evaluated under controlled, comparable settings, counteracting the historic fragmentation in the field due to inconsistent domains and evaluation protocols.

• Modularity: Algorithms, benchmarks, metrics, and diagnostic tools are implemented as separate, extensible modules.
• Compatibility: Full integration with Gymnasium-based environments is ensured through companion package gr-envs, facilitating evaluation across a spectrum of discrete (e.g., MiniGrid), continuous (e.g., highway-env’s Parking, Panda-Gym), and custom domains.
• Open Source: Both gr-libs and gr-envs are distributed via GitHub and PyPI, allowing direct adoption for research replication or extension.

2. Algorithmic Implementations and Online Dynamic Goal Recognition (ODGR) Formulation

gr-libs includes a portfolio of MDP-based and metric-learning GR algorithms:

• MDP-based baselines: GRAQL, DRACO, and their goal-conditioned counterparts GC-DRACO/GC-AURA.
• Metric-learning approaches: GRAML and its variants (BG-GRAML, GC-GRAML).

ODGR is formalized in gr-libs as tuples ⟨T, ⟨Gᶦ, {O}ᶦ⟩⟩, where T represents the environment’s domain theory, Gᶦ the dynamically adapting candidate goal sets, and {O}ᶦ the collection of observation traces. Each algorithm assumes this structured format, abstracting away domain-specific idiosyncrasies and enabling plug-and-play experimentation.

Benchmark specifications—including observability rates (e.g., 30%, 50%, 70%, 100%), trace noise/optimality, and sequence sampling strategies (consecutive vs. non-consecutive)—are defined in configuration files (consts.py), ensuring controlled, repeatable comparisons.

3. Diagnostic and Evaluation Utilities

gr-libs provides comprehensive diagnostic capabilities:

• Quantitative Metrics: Algorithmic outputs are evaluated by accuracy, inference time, candidate ranking, and, for complex methods, utilization patterns for precomputed caches.
• Qualitative Diagnostics: Experiment artifacts such as recognized goal predictions, rendered agent trajectories (images or videos), and detailed evaluation reports (text or pickled objects) are systematically archived.
• Parallelization: Native multi-core execution accelerates large-scale benchmarking, allowing simultaneous evaluation of multiple algorithms or experimental conditions.

Diagnostic outputs can be used for deep error analysis—e.g., inspecting systematic misclassifications as a function of observational noise or sub-optimal agent behavior.

4. Integration with Gym-Compatible Environments

gr-libs’ interoperability with gr-envs ensures that environments supply consistent representations of state, action, and goal information:

• Wrappers and Initializations: Custom wrappers unify the interface for both RL-based and direct planning environments, supporting approaches reliant on fast simulation or replay buffers.
• Example Pipeline: In illustrative usage (as shown in Listing lst:manual-odgr), a MiniGrid environment is set up with a Tabular Q-Learning actor, observation traces (full and partial) are generated, and the GRAQL recognizer is used to infer the most likely goal given a partial trace—demonstrating closed-loop interaction between agent, environment, and recognizer.

The table below summarizes components:

Component type	Example in gr-libs	Role
Recognizer	GRAQL, DRACO, GC-GRAML	Infers likely goal from observations
Actor/trainer	TabularQLearner, RL agents	Generates trajectories/traces
Diagnostic tool	Trajectory renderer, metrics	Evaluation and debugging

5. Standardization and Advancing Reproducible Research

By enforcing uniform handling of environment state, observation sequence, and goal adaptation phases, gr-libs explicitly structures ODGR experiments:

• Domain learning: RL or planning agents acquire behavior policies.
• Goal adaptation: Candidate sets adapt dynamically, enabling tests of algorithm robustness to changing objective spaces.
• Inference: Recognizers estimate the intended goal, typically under partial and potentially noisy observation.

Standardized metrics and artifact retention enable direct, reproducible comparisons across methods and experimental runs, accelerating cumulative progress and improved benchmarking.

6. Impact, Adoption, and Future Directions

gr-libs is intended to unify a previously fragmented field. Its standardization of benchmarks, environments, and metrics directly mitigates previous issues of non-comparability in GR research (Matan et al., 27 Sep 2025). The framework’s extensibility and diagnostic utilities lower the barrier for prototyping and analyzing new algorithms.

The roadmap includes:

• Expanding the environment set, particularly for real-world domains such as human-robot interaction and security.
• Enhanced integration with reinforcement learning toolkits (e.g., Stable-Baselines3).
• Support for parallel workflows and large-scale red-teaming/continuous benchmarking.

This suggests that gr-libs is positioned as a community infrastructure layer for dynamic GR, fostering consolidation of techniques and accelerating advances in model-free, online goal inference methodologies.