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Escaping Path Mirages in Offline Goal-Conditioned Reinforcement Learning

Junhyeon Bae, Jaebak Hwang, Gwanwoo Choi, Minung Kim, Seungyul Han

arXiv:2610.40130v1cs.LGcs.AI

TL;DR

Offline GCRL remains difficult in stochastic long-horizon settings, where path mirage can make misleading trajectories appear successful. BGP plans over branching-aware landmarks and goal-conditioned edges, and outperforms prior state-of-the-art methods on OGBench, particularly on challenging long-horizon tasks.

  • Problem

    In stochastic long-horizon offline GCRL, rare lucky outcomes can make trajectories seem reliably goal-reaching, encouraging overcommitment and compounding value errors.

    Source: §1 · p. 1

  • Method

    BGP identifies high-variability states as landmarks, constructs branch-aware goal-conditioned graph edges, and uses segmented learning to limit value propagation across unreliable branches.

    Source: §1 · pp. 1–2

  • Results

    BGP consistently improves over existing offline GCRL methods on OGBench, particularly on challenging long-horizon tasks.

    Source: §1 · p. 2, §7 · p. 10

  • Takeaways & Limitations

    BGP supports reliable goal-conditioned control in challenging long-horizon tasks by capturing branching structure and estimating local values reliably.

    Source: §7 · p. 10

    More sophisticated state representations may further improve BGP in high-dimensional manipulation settings.

    Source: §6 · p. 10

Abstract

from arXiv · show

Offline goal-conditioned reinforcement learning remains challenging in stochastic long-horizon settings, where compounding value estimation errors hinder reliable goal reaching. While prior methods have sought to address this challenge through various approaches, a key challenge remains path mirage, where agents overcommit to spuriously successful trajectories in offline datasets induced by stochasticity, often leading to failure on difficult tasks. To address this issue, we propose Branch-aware Graph Planning (BGP), which captures stochastic branching structures by identifying high-variability points as graph nodes and designing goal-conditioned edges with segmented learning to avoid unreliable branches and reduce unnecessary stochasticity along paths. As a result, BGP enables reliable goal reaching even in highly stochastic long-horizon environments, and experiments on diverse OGBench tasks show that it substantially outperforms prior state-of-the-art offline goal-conditioned RL methods. Our implementation is available at https://umlic.github.io/papers/BGP/.

1 Introduction

Offline GCRL struggles in stochastic long-horizon settings, where misleading trajectories and compounding value errors undermine goal reaching. BGP models branching explicitly and outperforms prior state-of-the-art methods, especially on stochastic long-horizon tasks.

1 Introduction

  • Offline GCRL extends offline RL to applications requiring agents to reach diverse user-specified goals, but remains challenging when stochasticity and long horizons compound value-estimation errors.Offline RL learns from static datasets when additional environment interaction is costly or unsafe [1] [2] [3].Source: §1 · p. 1
  • Path mirage occurs when rare lucky outcomes make trajectories appear reliably goal-reaching, leading agents to overcommit to misleading paths and propagate overly optimistic values.The problem worsens over long horizons as local branching errors accumulate.Source: §1 · p. 1
  • BGP consistently outperforms prior state-of-the-art offline GCRL methods on OGBench, with particularly strong gains on stochastic long-horizon problems.The paper frames these as settings where path mirage poses a major challenge.Source: §1 · p. 2
  • BGP identifies high-variability branching points as graph landmarks and builds goal-conditioned edges that encode reliable progress toward goals.Segmented learning limits value propagation across unreliable branches to mitigate stochasticity-induced errors.Source: §1 · pp. 1–2

2 Related Work

Prior offline GCRL work addresses value estimation, trajectory generation, reachability, and hierarchical subgoals, while graph methods support long-horizon planning. BGP distinguishes itself by using stochasticity to assess graph reliability.

Offline goal-conditioned reinforcement learning

  • Offline GCRL methods address overestimation and long-horizon generalization through conservative or expectile value learning, supervised trajectory methods, reachability objectives, and hierarchical subgoals.The passage identifies these as complementary research directions.Source: §2 · p. 2

Graph-based planning for goal-conditioned RL

  • Graph-based methods use connectivity structures for waypoint planning, while BGP adds reliability reasoning under stochastic branching to construct landmarks and goal-conditioned edges.Prior offline graph approaches exploit data-supported connectivity [8].Source: §2 · p. 2

Uncertainty-aware reinforcement learning

  • Uncertainty-aware RL commonly regularizes value learning or control risk, whereas BGP uses stochasticity as a structural signal for branching landmarks and graph construction.Source: §2 · p. 2

3 Preliminaries

The preliminaries specify offline goal-conditioned learning and graph-based planning as the two foundations for BGP. Policies learn from a static trajectory dataset, while graph search turns landmarks into sequential waypoints.

3.1 Offline Goal-Conditioned RL Setup

  • The offline GCMDP uses state-space goals and learns a policy from a trajectory dataset without additional environment interaction, under a reward of −1 before reaching the goal and 0 upon reaching it.Source: §3.1 · p. 2
  • GCIQL learns a goal-conditioned policy by maximizing a value interpreted as negative temporal distance; HIQL extends this approach hierarchically.Training goals are sampled from the current state, a future state in the same trajectory, or a random dataset state.Source: §3.1 · pp. 2–3

3.2 Graph-based Planning for Goal Conditioned MDPs

  • Graph planning represents landmarks and reachability-cost edges as a directed graph, then uses shortest-path search to produce waypoints for the underlying goal-conditioned policy.The policy advances to the next waypoint once the current one is reached.Source: §3.2 · p. 3

4 Methodology

BGP addresses path mirages by representing stochastic branching in a goal-conditioned graph and planning through reliable routes. It combines branch-aware landmarks and edges with locally composed distance estimates and segmented learning to limit unreliable value propagation.

4.1 Motivation: Path Mirage in Offline Goal-Conditioned RL

  • Offline trajectories can hide divergent futures, allowing occasional lucky successes to make unreliable paths appear dependable and sustain persistent failures.BGP treats highly stochastic branching points as landmarks and selects goal-conditioned routes based on the reliability of their downstream paths.Source: §4.1 · pp. 3–4

4.2 Branch-aware Graph Construction

  • Landmarks concentrate in high-uncertainty regions, while sparse regions need not be uniformly covered because they typically induce little future trajectory uncertainty.The method scores multi-step predictive uncertainty using discounted predictive entropies, then selects high-uncertainty states for landmark construction.Source: §4.2 · p. 4
  • Goal-conditioned edges assess all downstream branches, penalizing or removing transitions when any reachable branch worsens goal reachability or cannot reach the goal.This differs from local connectivity: a nearby transition can be rejected when its stochastic outcomes include an unreachable dead zone.Source: §4.2 · p. 5

4.3 Stability-oriented Distance Design and Graph Planning

  • BGP composes locally reliable landmark transitions into a stability-oriented distance, replacing one long-horizon value estimate with short-horizon predictions.It uses a pre-graph for distance estimation and a branch-aware graph for waypoint planning, with Dijkstra’s algorithm used to compute shortest paths.Source: §4.3 · pp. 5–6

4.4 Segmented Learning

  • Segmented learning trains on trajectory windows bounded by cluster transitions, limiting value propagation across stochastic boundaries.The window length M trades off local trajectory coverage against learning stability.Source: §4.4 · p. 6
  • BGP combines explicit reasoning about stochastic branching with risk-avoiding transitions and segmented learning for reliable goal-reaching.The framework overview is shown in Figure 4.Source: §4.4 · p. 6, Figure 4 · p. 6

5 Experiment

On OGBench, BGP outperforms prior offline GCRL methods and maintains strong performance across stochastic settings. Its path analysis and robustness tests highlight the roles of goal-conditioned edges and segmented learning.

5.1 Experimental Setup

  • Evaluation spans OGBench large, giant, and teleport mazes across antmaze, pointmaze, and visual-antmaze, using navigate, stitch, and explore datasets and comparing against offline GCRL baselines.Methods were trained for 500K steps on pixel-based tasks and 1M steps otherwise; BGP reports its best configuration over searched key hyperparameters.Source: §5.1 · p. 7

5.2 Performance Comparison

  • BGP achieves mean success rates of 91% on antmaze, 98% on pointmaze, and 86% on visual-antmaze, substantially exceeding HIQL.The advantage is especially evident in large and giant layouts, where prior methods achieve only partial success across goals.Source: §5.2 · pp. 7–8

5.3 Path Analysis under Graph Planning

  • With prior edges, locally reachable teleport paths fail around 33%; goal-conditioned edges use downstream reachability to avoid dead zones while retaining needed teleports and supporting stable paths across layouts.The comparison uses the same landmark set, with representative paths shown in Figure 6.Source: §5.3 · p. 8, Figure 6 · p. 8

5.4 Robustness to Increasing Stochasticity

  • At B=4, BGP reaches approximately 90% stitch success, about 10 percentage points above BGP without segmented learning.Both BGP variants maintain high success as teleport count increases on navigate datasets, while HIQL drops substantially; shaded regions show 95% confidence intervals over five seeds.Source: §5.4 · p. 9, Figure 7 · p. 9

5.5 Further Analysis of BGP

  • BGP Default outperforms all evaluated component variants; prior-edge and value-based-distance variants cause severe drops, while one-step estimation and random landmarks also degrade performance.The ablations are reported on antmaze-teleport-navigate and stitch.Source: §5.5 · p. 9, Table 2 · p. 10
  • Hyperparameter sensitivity varies by task: performance is broadly stable across landmark counts, while larger α hurts teleport-stitch tasks, where smaller α better captures concentrated branching.N ∈ [50, 100] provides a practical coverage–stability balance; the paper also reports modest computational overhead and strong performance with a modest search budget.Source: §5.5 · pp. 9–10

6 Limitation

BGP adds tuning and computational costs, and its state representations may be improved for high-dimensional manipulation.

6 Limitation

  • In high-dimensional manipulation, more sophisticated state representations may improve BGP’s performance; this remains future work.The paper also notes additional hyperparameters and graph-construction and planning overhead, while reporting practical tuning guidance and modest overhead relative to training cost.Source: §6 · p. 10

7 Conclusion

BGP combines branch-aware graph planning with segmented learning to support reliable goal-reaching in stochastic, long-horizon tasks. The appendix details its uncertainty model, landmark construction, planning, and local learning procedure.

7 Conclusion

  • BGP combines uncertainty-guided landmark construction, goal-conditioned graph planning, and segmented learning, with experiments showing consistent improvements on OGBench, particularly on challenging long-horizon tasks.The conclusion describes stable waypoint planning through a two-phase graph structure and reliable local value estimation.Source: §7 · p. 10

A.1 Graph Construction Detail

  • The autoregressive Transformer predicts future transitions with conditional Gaussian variance for entropy-based uncertainty, trains by teacher-forced negative log-likelihood, and feeds predictions back to estimate multi-step uncertainty.The discounted sum of conditional entropies over future steps gives U(n)(s_t).Source: §A.1 · p. 15, Figure 9 · p. 15
  • High-uncertainty states are projected and clustered; each landmark is an actual dataset state near a cluster center, and goal-conditioned edge costs use shifted expected distance reduction for Dijkstra planning.The representation is PCA for state-based antmaze and pointmaze, and a frozen Hilbert representation for other environments.Source: §A.1 · p. 15

A.2 Offline Learning Detail with Segmented Learning

  • Segmented learning trains value and policy on windows of M consecutive sub-trajectories; small windows can miss useful transitions, while large ones accumulate stochasticity and value bias.The figure depicts trajectory partitions at cluster transitions and concatenation into local windows.Source: §A.2 · p. 16, Figure 10 · p. 16
  • HIQL executes the planner’s waypoint path sequentially, with a high-level policy selecting closer subgoals and a low-level policy acting to reach them.Within each local window, goals are sampled from the current state, a future state, or a random state in that window.Source: §A.2 · p. 16

A.3 Environmental Details

The appendix describes OGBench environments and datasets, the methods used as baselines, shared and environment-specific BGP settings, and additional teleport-maze data construction.

A.3 Environmental Details

  • Evaluation covers OGBench point, ant, visual-ant, and humanoid mazes plus scene manipulation, using navigate, disconnected-segment stitch, and noisy, broad-coverage explore datasets.The giant humanoidmaze layout can require up to 3,000 environment steps.Source: §A.3 · pp. 17–18

A.4 Details of Baselines and Backbones

  • HIQL is a hierarchical goal-conditioned method with high- and low-level policies; GCIQL learns state- and action-value functions under the IQL framework.HIQL learns its value function with expectile regression and trains its two policies via advantage-weighted regression.Source: §A.4 · p. 19
  • The other baselines include CRL’s contrastive reachability learning, GAS’s offline graph search, TMD and QRL’s quasimetric approaches, and CGCIVL’s conservative value objective.These methods address transition or distance estimation, graph stitching, or value overestimation in offline goal-reaching.Source: §A.4 · pp. 19–20

A.5 Hyperparameter Setup

  • BGP defaults to Transformer context length n = 5 and local segment length M = 3 across all tasks, balancing sufficient multi-step stochasticity and computational efficiency during localized learning.For Hilbert-representation tasks, each local segment must contain at least 60 states, and an edge is retained only if its transition is observed at least 20 times.Source: §A.5 · p. 20

B.1 Teleport Environments with Varying Numbers of Blackholes

  • The teleport mazes vary the number of active blackholes; entering one sends the agent uniformly at random to one of three fixed exits, with velocity preserved.Navigate and stitch datasets use a noisy pretrained SAC policy following a shortest-path oracle that ignores teleports.Source: Figure 12 · p. 21, §B.1 · p. 21

B.2 Performance on Additional Tasks

Additional evaluations report higher BGP success rates than compared baselines across five humanoidmaze datasets and on scene-play manipulation. The humanoidmaze gains are especially large on giant layouts.

Additional evaluation setup

  • Both additional evaluations use landmark clustering in the Hilbert representation space.The tasks are humanoidmaze and scene-play.Source: §B.2 · p. 22

Manipulation

  • 60% mean success rate on scene-play, compared with 38% for HIQL, 51% for GCIQL, 5% for QRL, and 19% for CRL.This manipulation evaluation supplements the navigation experiments.Source: §B.2 · p. 22, Table 6 · p. 22

Humanoidmaze

  • BGP achieves higher mean success rates than compared baselines on five humanoidmaze datasets, with particularly large improvements on giant layouts.Table 6 reports success rates for navigate and stitch datasets on medium, large, and giant layouts.Source: Table 6 · p. 22, §B.2 · p. 22

B.3 Trajectory Analysis

Trajectory visualizations show BGP routing around stochastic teleport regions and organizing navigation through landmarks in giant and large mazes. The examples illustrate branch-aware waypoint planning across layouts.

Teleport layout

  • In teleport mazes, BGP identifies branching points and routes around teleportation zones rather than treating them as spuriously efficient shortcuts.The planner evaluates expected downstream branch outcomes to construct a reliable route.Source: §B.3 · p. 23, Figure 13 · p. 23

Giant layout

  • In the giant maze, BGP sequences reliable intermediate landmarks into a coherent waypoint trajectory toward a distant goal.The layout’s long corridors and branching paths make long-horizon planning challenging.Source: §B.3 · p. 23, Figure 14 · p. 23

Large layout

  • In the large maze, BGP identifies branching junctions as stochastic landmarks and composes a route to the target goal.The visualization illustrates that the graph remains informative in a smaller, structurally simpler environment.Source: §B.3 · p. 24, Figure 15 · p. 24

B.4 Hyperparameter Sensitivity

BGP remains robust across broad hyperparameter ranges, although sensitivity varies by environment. Segment length must balance value propagation against accumulated stochasticity; the authors use M = 3 by default.

B.4 Hyperparameter Sensitivity

  • Across α and N, BGP maintains consistently high success rates despite stronger variation in antMaze, especially teleport, than in pointMaze; Figs. 16–17 show teleport and giant.The section reports similar overall trends across multiple environments.Source: §B.4 · p. 25, Figure 16 · p. 25, Figure 17 · p. 25
  • Too-small M limits value propagation between stochastic branches, while excessive M accumulates estimation bias; GCIQL declines at M = 10, whereas HIQL is more robust but still degrades.Table 7's M = 10 results exclude stitch datasets because their trajectories are too short.Source: §B.4 · p. 25, Table 7 · p. 26
  • The authors select M = 3 to retain enough horizon for value propagation while avoiding accumulated estimation bias.Source: §B.4 · p. 25

B.5 Hyperparameter Search Budget

The budget-aware evaluation measures BGP performance as hyperparameter-search evaluations accrue. BGP exceeds the reported tuned baselines after one evaluation and reaches 80% success after 35 evaluations, near its 200-evaluation result.

B.5 Hyperparameter Search Budget

  • The budget-aware protocol evaluates 60 configurations across N, α, and M, simulating sequential UCB-bandit search on antmaze-teleport-navigate.Sixteen sampled policies are trained and evaluated per configuration; reported expected success is averaged over repeated searches.Source: §B.5 · p. 26
  • Performance gains diminish beyond 35 evaluations, suggesting extensive search is unnecessary to approach BGP's best achievable performance.Source: §B.5 · p. 26
  • 65% expected success after one evaluation exceeds tuned CRL (53%), HIQL (42%), and GAS (39%); BGP reaches 80% after 35 evaluations and 84% after 200.The 35-evaluation result corresponds to approximately 95% of the 200-evaluation performance.Source: Table 8 · p. 26, §B.5 · p. 26

B.6 Computational Cost Analysis

The section discusses BGP’s computational costs, preprocessing and dataset-quality limitations, interpretability, and reproducibility practices. It also addresses the NeurIPS LLM-use checklist, marking LLM use as [N/A] and describing use only for writing and editing.

B.6 Computational Cost Analysis

  • BGP’s preprocessing costs scale as O(|D|), O(U · N · I), and O(N^2 · C), while waypoint search takes O(N log N + |E|).These terms correspond respectively to predictive uncertainty scoring, K-means++ landmark extraction, branch-aware edge construction, and Dijkstra search; with 50 ≤ N ≤ 100 landmarks, the paper describes inference overhead as practically negligible.Source: §B.6 · p. 26
  • BGP has moderate simulation-time overhead over implicit value-learning methods, but is significantly more efficient than GAS and competitively efficient with TMD.The stated overhead comes from uncertainty estimation, clustering, and Dijkstra’s algorithm; implicit baselines do not construct explicit graphs or search paths.Source: §B.6 · pp. 26–27, Table 9 · p. 27

C Broader Impact

  • BGP may support autonomous-logistics planning and route verification, but depends heavily on offline dataset quality and adds preprocessing cost.The paper says BGP introduces no unique societal or ethical risks beyond standard offline reinforcement learning and requires standard responsible oversight.Source: C Broader Impact · p. 27

NeurIPS Paper Checklist

  • The checklist reports five-seed mean ± standard deviation for main benchmarks and 95% confidence intervals for stochasticity and budget-aware analyses.It says methods and implementation details are provided in Section 4 and Appendix A, with source code and running scripts in supplementary material; computational setup including GPU, memory, and operating system is in Appendix A.3.Source: NeurIPS Paper Checklist · pp. 29–31
  • The checklist marks LLM use [N/A], stating it was limited to writing and editing and excluded from core-method development.Source: NeurIPS Paper Checklist · p. 34

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