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Goal-Conditioned Reinforcement Learning: Problems and Solutions
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Goal-conditioned reinforcement learning (GCRL), related to a set of complex RL problems, trains an agent to achieve different goals under particular scenarios. Compared to the standard RL solutions that learn a policy solely depending on the states or observations, GCRL additionally requires the agent to make decisions according to different goals. In this survey, we provide a comprehensive overview of the challenges and algorithms for GCRL. Firstly, we answer what the basic problems are studied in this field. Then, we explain how goals are represented and present how existing solutions are designed from different points of view. Finally, we make the conclusion and discuss potential future prospects that recent researches focus on.
Forward citations
Cited by 26 Pith papers
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World Models in Pieces: Structural Certification for General Agents
Structural certification maps bounded goal-conditioned performance to O(1/n) + O(δ) entry-wise error bounds on an agent's internal world model for transitions filtered by deep compositional goals.
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Learning Object Manipulation from Scratch via Contrastive Interaction
IWR improves CRL sample efficiency and performance in interaction-rich manipulation by interaction-aware resampling that preserves mode boundaries, yielding 19.8% average gains and a real-world air-hockey agent.
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Dual Advantage Fields
Dual Advantage Fields converts bilinear dual value models into local advantage scores via learned action-effect models, equaling the goal-conditioned Bellman advantage under realizability and improving aggregate metri...
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CurveRL: Principled Distribution-Aware Context Reweighting for LLM Reasoning
CurveRL derives a quantile-coordinate reweighting rule from a utility functional on pass rates and shows it outperforms GRPO on reasoning benchmarks.
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SpecRLBench: A Benchmark for Generalization in Specification-Guided Reinforcement Learning
SpecRLBench is a new benchmark evaluating generalization of LTL-guided RL methods across navigation and manipulation domains with static/dynamic environments and varied robot dynamics.
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Coachable agents for interactive gameplay
A framework combining universal value function approximators with targeted training scenarios and data augmentation produces RL agents that adapt to user-specified styles in real time across video games and humanoid d...
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Decoupled Behavioral Cloning for Scalable Inductive Generalization in RL from Specifications
DIBS decouples task policy learning via RL from evolution function learning via behavioral cloning to achieve more stable training and better generalization than prior RL and meta-RL methods for inductive generalizati...
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Stochastic Minimum-Cost Reach-Avoid Reinforcement Learning
Introduces RAPCs and a contraction Bellman operator for cost-optimal policies that satisfy probabilistic reach-avoid specifications in stochastic MDPs, with almost-sure convergence to local optima.
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Stochastic Minimum-Cost Reach-Avoid Reinforcement Learning
Introduces RAPCs and a contraction Bellman operator that jointly enforce probabilistic reach-avoid constraints while minimizing expected costs in stochastic RL, with almost-sure convergence to local optima.
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Plan in Sandbox, Navigate in Open Worlds: Learning Physics-Grounded Abstracted Experience for Embodied Navigation
SAGE trains agents in physics-grounded semantic abstractions via RL with asymmetric clipping, achieving 53.21% LLM-Match Success on A-EQA (+9.7% over baseline) and encouraging physical robot transfer.
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QHyer: Q-conditioned Hybrid Attention-mamba Transformer for Offline Goal-conditioned RL
QHyer achieves state-of-the-art results in offline goal-conditioned RL by replacing return-to-go with a state-conditioned Q-estimator and introducing a gated hybrid attention-mamba backbone for content-adaptive histor...
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QHyer: Q-conditioned Hybrid Attention-mamba Transformer for Offline Goal-conditioned RL
QHyer replaces return-to-go with a state-conditioned Q-estimator and adds a gated hybrid attention-mamba backbone to achieve state-of-the-art performance in offline goal-conditioned RL on both Markovian and non-Markov...
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Improving Zero-Shot Offline RL via Behavioral Task Sampling
Extracting task vectors from the offline dataset for policy training improves zero-shot offline RL performance by an average of 20% over random sampling baselines.
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When Policies Cannot Be Retrained: A Unified Closed-Form View of Post-Training Steering in Offline Reinforcement Learning
For diagonal-Gaussian frozen actors, PoE with alpha equals KL adaptation with beta = alpha/(1-alpha); empirically, composition shows an actor-competence ceiling with 4/5/3 HELP/FROZEN/HURT split on D4RL and zero succe...
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Occupancy Reward Shaping: Improving Credit Assignment for Offline Goal-Conditioned Reinforcement Learning
Occupancy Reward Shaping extracts goal-reaching rewards from world-model occupancy measures using optimal transport, improving offline goal-conditioned RL performance 2.2x on 13 tasks without changing the optimal policy.
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AdaTracker: Learning Adaptive In-Context Policy for Cross-Embodiment Active Visual Tracking
AdaTracker enables zero-shot cross-embodiment active visual tracking by encoding embodiment constraints from history to modulate a context-aware policy.
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DreamPolicy: A Unified World-model Policy for Scalable Humanoid Locomotion
DreamPolicy integrates an autoregressive diffusion world model with policy learning to produce a single scalable policy that generalizes to unseen composite terrains for humanoid locomotion.
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Vision-Language Foundation Models as Effective Robot Imitators
RoboFlamingo adapts open-source vision-language models for robot manipulation tasks via single-step comprehension plus an explicit policy head, outperforming prior methods on benchmarks with only light fine-tuning.
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FootsiesGym: A Fighting Game Benchmark for Two-Player Zero-Sum Imperfect-Information Games
FootsiesGym is an open-source, vectorized fighting-game benchmark for two-player zero-sum imperfect-information RL that isolates non-transitive neutral-game dynamics while remaining tractable on standard hardware.
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Embodiment Shapes Rolling Behavior in a Multimodal Infant Model
A reinforcement learning model of a multimodal virtual infant produces rolling behaviors that reproduce age-related improvements and coordination patterns observed in human infants, shaped by changing body morphology.
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Goal-Conditioned Supervised Learning for LLM Fine-Tuning
GCSL reframes LLM fine-tuning as supervised pursuit of quality thresholds using natural-language goals, outperforming SFT and DPO on toxicity, code, and recommendation tasks.
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Improving Zero-Shot Offline RL via Behavioral Task Sampling
Extracting task vectors from offline data to define training task distributions improves zero-shot offline RL performance by an average of 20%.
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GCImOpt: Learning efficient goal-conditioned policies by imitating optimal trajectories
GCImOpt trains compact goal-conditioned neural policies by imitating efficiently generated optimal trajectories, achieving high success rates and near-optimal performance on cart-pole, quadcopter, and robot arm tasks ...
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Efficient Hierarchical Implicit Flow Q-learning for Offline Goal-conditioned Reinforcement Learning
Proposes mean flow policies and LeJEPA loss to overcome Gaussian policy limits and weak subgoal generation in hierarchical offline GCRL, reporting strong results on OGBench state and pixel tasks.
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Strict Subgoal Execution: Reliable Long-Horizon Planning in Hierarchical Reinforcement Learning
SSE improves long-horizon goal-conditioned RL by using failure and partial-success transitions to identify unreliable subgoals, streamline high-level planning, and outperform prior hierarchical methods on benchmarks.
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GUIDE: Goal-Initialized Directional Understanding for End-to-End Visual Navigation
GUIDE is an end-to-end RL policy for quadruped navigation that builds directional awareness from proprioceptive history via a spatial anchor predictor and raw depth, without ongoing goal inputs or maps.
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