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OGBench: Benchmarking Offline Goal-Conditioned RL

Seohong Park, Kevin Frans, Benjamin Eysenbach, Sergey Levine

arXiv:2410.20092v2cs.LGcs.AI

TL;DR

Offline GCRL offers a simple, unsupervised way to learn diverse behaviors from unlabeled data, but the field lacks a benchmark that tests its capabilities systematically. OGBench addresses this gap with diverse environments, datasets, and reference algorithms, revealing distinct strengths and weaknesses across challenging settings.

  • Problem

    Offline GCRL lacks a standard benchmark for systematically evaluating capabilities such as stitching, long-horizon reasoning, high-dimensional inputs, and stochasticity.

  • Method

    OGBench introduces 8 environment types, 85 datasets, and reference implementations of 6 representative offline GCRL algorithms designed around diverse algorithmic challenges.

  • Results

    Representative algorithms show stark, task-dependent strengths and weaknesses: HIQL is particularly strong in locomotion and visual manipulation, CRL in locomotion, GCIQL in manipulation, and GCIVL in drawing.

  • Takeaways & Limitations

    OGBench provides a foundation for developing and evaluating offline GCRL algorithms across capabilities that prior benchmarks insufficiently distinguish.

  • Takeaways & Limitations

    Current offline GCRL algorithms often struggle with long-horizon, high-dimensional, and stitching tasks, and no single method consistently outperforms the others across all tasks.

Abstract

from arXiv · show

Offline goal-conditioned reinforcement learning (GCRL) is a major problem in reinforcement learning (RL) because it provides a simple, unsupervised, and domain-agnostic way to acquire diverse behaviors and representations from unlabeled data without rewards. Despite the importance of this setting, we lack a standard benchmark that can systematically evaluate the capabilities of offline GCRL algorithms. In this work, we propose OGBench, a new, high-quality benchmark for algorithms research in offline goal-conditioned RL. OGBench consists of 8 types of environments, 85 datasets, and reference implementations of 6 representative offline GCRL algorithms. We have designed these challenging and realistic environments and datasets to directly probe different capabilities of algorithms, such as stitching, long-horizon reasoning, and the ability to handle high-dimensional inputs and stochasticity. While representative algorithms may rank similarly on prior benchmarks, our experiments reveal stark strengths and weaknesses in these different capabilities, providing a strong foundation for building new algorithms. Project page: https://seohong.me/projects/ogbench

1 MOTIVATION

Offline GCRL offers a simple, unsupervised route to learning generalist behaviors from unlabeled data, but its apparently simple objective is difficult and lacks standardized evaluation. OGBench addresses this gap with diverse, challenging environments, datasets, and reference algorithms.

  • Offline GCRL aims to learn policies that reach any dataset state from any other state without rewards or domain knowledge.Its simplicity makes it a candidate paradigm for data-driven, unsupervised reinforcement learning.
  • Mastering this objective requires diverse skills and a comprehensive understanding of the underlying world and dataset.
  • Existing offline GCRL research lacks a standard benchmark for evaluating stitching, long-horizon reasoning, and stochasticity.
  • OGBench provides 8 environment types, 85 datasets, and reference implementations of 6 representative offline GCRL methods.Its tasks are designed to expose diverse algorithmic challenges and contrasts across evaluation criteria.

2 PROBLEM SETTING

Offline GCRL is formulated on a reward-free controlled Markov process and an unlabeled trajectory dataset. The policy uses full states as goals and is trained to reach each goal in as few discounted steps as possible.

  • The problem uses a controlled Markov process M = (S, A, µ, p) without rewards together with an unlabeled dataset D of trajectories.S and A are the state and action spaces, while µ and p specify initial-state and transition distributions.
  • The learned goal-conditioned policy π(a | s, g) maps a current state and goal state to an action distribution.
  • The objective rewards reaching goal g through the discounted sum of goal-state indicators over an episode.The trajectory distribution is induced by the initial-state distribution, policy, and transition dynamics.
  • Benchmark task properties include environment type, task length, atomic behaviors, stitching, stochasticity, pixel observations, multiple goals, and dependency.
  • The entire state space serves as the goal space, so each goal is a full state rather than a selected state component.This choice makes the objective fully unsupervised and suitable for training from unlabeled data.

3 HOW HAVE PRIOR WORKS BENCHMARKED OFFLINE GCRL?

Prior offline GCRL studies use existing single-task offline RL benchmarks or simple policy-collected goal-conditioned tasks, leaving important multi-goal and complex offline challenges insufficiently evaluated.

  • The community lacks a standardized evaluation framework for offline GCRL despite many proposed algorithms.
  • Existing offline RL tasks such as D4RL AntMaze and Kitchen often evaluate multi-task policies on only their original single goal.Because these tasks were designed for single-task offline RL, they provide limited multi-goal evaluation.
  • Policy-collected Fetch-style tasks are mostly atomic and do not sufficiently test long-horizon reasoning or goal stitching.

4 OVERVIEW OF OGBENCH

OGBench is designed as a usable benchmark that exposes diverse offline GCRL challenges through complex tasks and datasets. It includes broad task coverage and reference implementations to support systematic algorithm development and evaluation.

  • OGBench provides tasks and datasets intended to unlock the potential of offline goal-conditioned RL.
  • Its benchmark challenges are designed so researchers can test and iterate on algorithmic ideas across diverse offline GCRL settings.
  • OGBench contains 8 environment types, 85 datasets, and reference implementations of 6 representative offline GCRL algorithms.
  • The benchmark aims for complex behaviors to emerge when tasks are successfully solved.

5 CHALLENGES IN OFFLINE GOAL-CONDITIONED RL

Offline GCRL is simple and unsupervised but difficult because effective learning must handle suboptimal data, goal stitching, long horizons, and stochastic dynamics.

  • Learning from suboptimal, unstructured data: Offline GCRL should learn effective multitask policies from diverse, suboptimal data rather than relying only on curated expert demonstrations.Such data is cheaper to collect and aligns with the role of large, diverse, unstructured datasets in modern machine learning.
  • Goal stitching: Goal stitching combines initial and final states from different trajectories to produce diverse behaviors, even when demonstrations are individually optimal.For example, separate atomic pick-and-place behaviors can be combined into a sequential multi-object task.
  • Long-horizon reasoning: Long-horizon reasoning requires navigating between states separated by many steps, as in autonomous driving or multi-subtask assembly.OGBench tasks are more than 5 times longer than previous tasks in both episode length and number of subtasks.
  • Handling stochasticity: Offline GCRL must handle stochastic environments, although many prior methods assume deterministic dynamics to exploit temporal-distance structure or hierarchical control.Those assumptions can produce optimistic behavior in settings where transitions are uncertain.

6 DESIGN PRINCIPLES

OGBench is designed as a realistic, difficult, scientifically controllable, computationally accessible, and easy-to-use benchmark for offline GCRL research.

  • Realistic and exciting tasks: OGBench tasks are intended to be realistic and exciting while covering long-horizon reasoning, stochastic control, and combinatorial generalization through goal stitching.The benchmark targets behaviors such as puzzle solving, maze navigation, and painting with different brushes.
  • Appropriate difficulty: Tasks must be difficult enough to distinguish algorithms while remaining solvable from the provided datasets.Tasks that are too easy, too hard, or insufficiently discriminative provide weak benchmarking signals.
  • Controllable datasets: Controllable datasets support rigorous analysis and ablation studies instead of relying only on fixed human-collected data.The benchmark prioritizes tools that let researchers adjust datasets to study algorithmic challenges scientifically.
  • Minimal compute requirements: The benchmark minimizes unnecessary computational overhead so researchers can iterate quickly while retaining challenging, solvable tasks.The intended focus is algorithmic difficulty rather than expensive high-resolution image processing.
  • High code quality: Clean, well-tuned reference implementations and minimal dependencies make OGBench easy to set up and reuse.The environments depend only on MuJoCo, while algorithm implementations minimize file dependencies.

7 ENVIRONMENTS, TASKS, AND DATASETS

OGBench spans locomotion, manipulation, and drawing, with multiple observation modalities, evaluation protocols, task variants, and datasets targeting distinct offline GCRL capabilities.

  • Benchmark structure: OGBench contains locomotion, manipulation, and drawing environments, with separate validation datasets and mostly both state- and pixel-based observations.The benchmark evaluates each task using five predefined state-goal pairs and average success rate across those goals.
  • Evaluation: Evaluation episodes receive a full-state goal and terminate when the agent reaches it under each task’s success criterion.Initial and goal states are slightly randomized across multiple rollouts for each predefined pair.
  • Locomotion tasks: Locomotion includes PointMaze, AntMaze, HumanoidMaze, and AntSoccer, targeting long-horizon and hierarchical reasoning.The agents range from a 2-D point mass to an 8-DoF Ant and a 21-DoF Humanoid.
  • Locomotion tasks: OGBench extends D4RL maze tasks with Humanoid control, pixel observations, multi-goal evaluation, and more challenging maze and dataset variants.These additions explicitly address capabilities that the original tasks did not challenge, including stitching and stochasticity.
  • Locomotion tasks: Maze variants include giant layouts with paths up to 3000 environment steps and teleport layouts with stochastic transitions and dead-end risks.The teleport setting tests whether agents avoid black holes rather than exploiting lucky outcomes.
  • Locomotion datasets: Maze datasets include navigate, stitch, and explore variants, with stitch trajectories requiring up to 8 short trajectories to be combined.These datasets impose different challenges through noisy navigation, short goal-reaching segments, and exploration behavior.
  • Observation modalities: Pixel-based settings use 64×64×3 RGB images, and locomotion pixels omit low-dimensional state information such as joint angles.The visual setup requires the agent to learn purely from images, while floor coloring helps infer location.
  • Locomotion tasks: AntSoccer requires dribbling a ball while navigating, with stitch datasets separating maze navigation from ball dribbling so full-task completion requires stitching.It is substantially harder than AntMaze because the agent must control both locomotion and the ball.

8 RESULTS

OGBench benchmarks six offline GCRL methods across diverse tasks, using multi-goal success rates and category-level aggregation to expose method-specific strengths in stitching, stochasticity, and visual observations. No single method dominates: HIQL is broadly strong, while other methods excel in particular settings.

  • Algorithms: OGBench evaluates six representative offline GCRL algorithms using average binary success rates across five test-time goals.Results are averaged over 8 seeds, or 4 seeds for pixel-based tasks, with standard deviations reported in Table 2.
  • Overall performance: HIQL tends to perform strongly across categories, while CRL leads locomotion and GCIVL or GCIQL lead other task groups.More specifically, CRL tends to work best in locomotion, GCIQL in manipulation, and GCIVL in drawing.
  • Goal stitching: HIQL outperforms CRL on stitching-oriented datasets, with their relative performance reversing on visual locomotion stitch tasks.The comparison contrasts full RL-based methods fitting Q* with one-step methods fitting the behavioral value function Qβ.
  • Stochasticity: Value-only methods such as HIQL and QRL struggle more on stochastic teleport mazes, whereas CRL is generally robust to environment stochasticity.The teleport and large mazes share maze size, but teleport introduces stochastic transitions that incur risk.
  • Pixel-based observations: CRL is notably robust across state and pixel observations, while HIQL is strong in visual manipulation but relatively weak on image drawing tasks.The authors attribute HIQL’s drawing weakness to difficulty learning low-dimensional subgoal representations from high-intrinsic-dimensionality states.
  • Reference implementations: Reference implementations generally outperform previously reported results on D4RL antmaze-large, sometimes substantially, as with CRL.The comparison uses previously reported numbers from prior offline GCRL studies.
  • Evaluation: Multi-goal evaluation can reverse method rankings and produce clearer performance differences than single-goal evaluation on the same maze.On antmaze-large, the ranking between GCIQL and QRL reverses under multi-goal evaluation, while OGBench shows more pronounced differences.
  • Data collection: Adding the right amount of Gaussian action noise is important for dataset coverage: cube pick-and-place performance drops from 99% to 6% without noise.The ablation varies noise strength on cube-single-noisy and puzzle-3x3-noisy manipulation tasks.

9 RESEARCH OPPORTUNITIES

OGBench exposes unresolved research questions about generalization, cross-category performance, subgoal representations, value learning, and goal sampling, while leaving challenging tasks unsolved for current methods.

  • Unsolved tasks: OGBench includes challenging tasks such as humanoidmaze-giant, cube-triple, puzzle-4x5, and powderworld-hard where no existing method achieves non-trivial performance.The benchmark provides sufficient data for these tasks based on estimates from easier variants.
  • Generalization: Hierarchical RL methods such as HIQL perform especially well on several tasks, motivating study of whether separate specialized policies improve test-time generalization.The authors hypothesize that hierarchical decomposition reduces learning complexity and helps both policies generalize better.
  • Cross-category performance: No method consistently performs best across categories: HIQL struggles in pixel locomotion and state manipulation, GCIQL in locomotion, and CRL in manipulation.These contrasting strengths motivate methods that combine category-specific advantages.
  • Task-specific questions: PointMaze is unexpectedly difficult, while QRL significantly outperforms other methods there, raising questions about the source of this task-specific difference.The authors identify subgoal representations as one possible explanation for these differing results.
  • Subgoal representations: Subgoal representation learning remains open because HIQL's additional policy-loss signal helps in pixel environments but not state-based environments.The paper asks whether more stable representations can be learned for hierarchical RL and planning.
  • Value learning and goal sampling: Value-learning choices show an unresolved trade-off: CRL outperforms GCIQL on locomotion, whereas GCIQL is much better than CRL in manipulation.The paper also questions whether policy goals should generally be sampled only from future states in the current trajectory.

10 OUTLOOK

OGBench is presented as a foundation for studying offline GCRL as general-purpose RL pre-training. Experiments indicate meaningful promise alongside substantial algorithmic and benchmark limitations.

  • Benchmark role: OGBench is introduced as a benchmark intended to advance algorithms research in offline goal-conditioned RL.It provides a foundation for evaluating and developing methods in this setting.
  • Potential of offline GCRL: Offline GCRL may support general-purpose RL pre-training by learning diverse, effective policies from highly suboptimal unlabeled data and sparse rewards.The experiments show this potential across realistic simulated robotics and game-like settings, including exceptionally long-horizon tasks.
  • Policy outcomes: Offline GCRL can achieve complex outcomes, such as beating a puzzle game, that cannot be achieved simply by copying random data.This contrasts policy learning with generative objectives that capture the data distribution.
  • Current limitations: Current algorithms often struggle with long-horizon, high-dimensional, and stitching tasks, and no single method consistently outperforms others.The paper identifies establishing a challenging, diverse benchmark as the first step toward better algorithms.
  • Benchmark scope: OGBench does not test changing environment dynamics or visual generalization to entirely new objects, and it sacrifices some realism to reduce computational cost.Examples include transparent manipulation arms and synthetic but controllable datasets.

B ADDITIONAL DATASETS

OGBench augments its manipulation suite with noisy datasets and broad task variants, while documenting the goal-sampling and algorithmic components used in offline GCRL.

  • Additional manipulation datasets: Play datasets use open-loop, non-Markovian expert policies with temporally correlated noise, whereas noisy datasets use closed-loop, Markovian experts.The noisy variants support dataset ablations and comparisons between non-Markovian and Markovian data.
  • Additional manipulation datasets: Manipulation performance is mostly similar across play and noisy datasets, but some methods struggle with narrower, non-Markovian play trajectories.GCIQL nearly solves cube-single-noisy but struggles on cube-single-play.
  • Goal sampling: Four goal-sampling distributions use current states, uniform future states, geometric future states, or random dataset states.The distributions are mixed through hyperparameters for some objectives.
  • Offline GCRL algorithms: GCBC trains a goal-conditioned policy by behavioral cloning with future states from the same trajectory as goals.Its objective directly maps state-goal pairs to dataset actions.
  • Offline GCRL algorithms: GCIVL and GCIQL are goal-conditioned IQL variants, with GCIVL fitting only V and GCIQL fitting both V and Q through expectile-based learning.GCIQL is unbiased in stochastic environments, whereas GCIVL is optimistically biased.
  • Offline GCRL algorithms: QRL models goal-conditioned values as quasimetric distances and, for continuous actions, jointly learns a latent dynamics model and representation.It extracts policies with AWR in discrete-action settings and uses a DDPG+BC-like objective with the learned dynamics model for continuous actions.
  • Task variants: Puzzle tasks ensure test-time goals are solvable, while OGBench also supplies 410 single-task variants and oracle-representation variants for diagnosis and standard offline RL.Each locomotion and manipulation environment provides five single-task variants, and oracle representations retain only goal-relevant information.

E.4 METHODS

OGBench standardizes implementation and evaluation procedures across its datasets, while exposing environment, dataset, task, and policy-extraction specifications.

  • Implementations: Six JAX implementations benchmark GCBC, GCIVL, GCIQL, QRL, CRL, and HIQL.Typical runs take 2–5 hours on state-based tasks and 5–12 hours on pixel-based tasks using an A5000 GPU.
  • Evaluation: Performance is measured by goal success rate using 50 rollouts per test-time goal every 100K steps, averaged over the final three evaluation epochs.The reported average covers 750 rollouts: three epochs, five goals, and 50 rollouts each.
  • Hyperparameters: Value-learning hyperparameters are mostly shared across datasets, while policy-extraction hyperparameters are individually tuned because they are more sensitive.Examples include AWR α and the DDPG+BC coefficient.
  • Pixel-based implementation: Pixel-based environments use a smaller IMPALA encoder, and random-crop augmentation is applied only to pixel-based manipulation tasks.Frame stacking is omitted in pixel-based environments.
  • Benchmark specifications: The benchmark documentation includes dataset categories, environment specifications, dataset specifications, and designated default single-task environments.The default task is the most representative of the five evaluation-goal variants.
  • Benchmark specifications: Tables 10 and 11 report common hyperparameters and policy-extraction settings, including the extraction method and α value.The α value denotes either AWR temperature or the DDPG+BC behavior-cloning coefficient.

F EVALUATION GOALS AND PER-GOAL BENCHMARKING RESULTS

OGBench evaluates each task across five test-time goals and provides per-goal visual descriptions and complete benchmarking tables spanning maze, soccer, manipulation, drawing, and pixel-based tasks.

  • Evaluation goals: Each OGBench task provides five evaluation goals with image descriptions in Figures 4–10 and per-goal results in Tables 12–24.The tables share a common format for reporting goal-specific benchmark outcomes.
  • Locomotion goals: Figures 4 and 5 depict goals for PointMaze, AntMaze, HumanoidMaze, and AntSoccer environments.These figures cover locomotion and soccer goal configurations.
  • Benchmark results: Tables 12–17 report full results for PointMaze, AntMaze, HumanoidMaze, AntSoccer, Visual AntMaze, and Visual HumanoidMaze.The table set spans state- and pixel-based locomotion environments.
  • Benchmark results: The per-goal results include Powderworld alongside the locomotion and manipulation suites.Powderworld is reported in Table 24.
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