Source-linked AI summary
Goal-Conditioned Reinforcement Learning: Problems and Solutions
Minghuan Liu, Menghui Zhu, Weinan Zhang
TL;DR
GCRL addresses the challenge of learning policies that achieve different goals rather than a single reward-defined task. This survey formulates the problem, reviews goal representations and learning solutions, and summarizes the field’s development and future prospects. It identifies generalization and sample efficiency, especially under sparse rewards, as central concerns.
Problem
GCRL must achieve arbitrary goals across tasks, creating challenges from sparse rewards, multi-task sample efficiency, and generalization.
Method
The survey organizes GCRL algorithms by goal representation and by learning phases including optimization, sub-goal generation, and relabeling.
Results
The survey reviews GCRL’s formulation, goal types, challenges, algorithms, and future prospects.
Takeaways & Limitations
GCRL is treated as multi-task learning in which task variation comes from goal-induced rewards under shared environment dynamics.
Takeaways & Limitations
The survey notes that GCRL formulations and variants extend beyond the material discussed.
Abstract
from arXiv · showhide
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.
1 Introduction
GCRL extends reinforcement learning from single-task policies to policies conditioned on goals, enabling multiple tasks and challenging goal achievement. The survey organizes its review around GCRL’s challenges, goal representations, learning solutions, and future prospects.
- GCRL augments observations with an additional goal that the agent must achieve when making decisions.
- Unlike standard RL, GCRL addresses multiple goals while requiring generalization across tasks and sample-efficient learning.Sparse rewards and multi-task sample efficiency are identified as central challenges.
- Goals representations: Goals may be represented as state subspaces, images, feature vectors, language, rewards, commands, or intrinsic skills.
- Solutions towards learning phases: A typical GCRL training loop selects or receives a goal, collects trajectories, optionally relabels goals, and optimizes goal-augmented policy and value functions.
- The survey covers basic challenges, goal representations, learning-phase solutions, and future prospects for GCRL.
2 Problems and Challenges
GCRL formulates multi-goal decision-making by augmenting an MDP with goals and a state-to-goal mapping. Its main challenges are generalization across tasks and sample efficiency under sparse rewards, addressed through unified policies, goal structures, and reward-processing methods.
- Standard RL learns a policy for one reward-defined task, whereas GCRL can master multiple tasks or decompose long-term goals into sub-goals.
- Goal-Augmented MDPs: A goal-augmented MDP adds goal space G, desired-goal distribution p_g, and mapping φ:S→G, with a goal-conditioned policy maximizing return over goals.
- Goal definitions: Desired goals specify required tasks, achieved goals describe the current state’s result, and behavioral goals target rollout sampling.
- Goal definitions: Behavioral goals should equal desired goals when no sub-goal replacement occurs.
- Generalization: Learning over Multiple Tasks: GCRL generalizes multi-task learning because different goals induce different rewards under shared environment dynamics.
- Generalization: Learning over Multiple Tasks: Shared goal properties allow goal-conditioned policies to share parameters across tasks and derive generalized solutions.
- Sample Efficiency: Towards Sparse Rewards: Sparse binary rewards provide little information after unsuccessful episodes, motivating distance-based reward shaping.
- Sample Efficiency: Towards Sparse Rewards: Dense distance-based shaping can alleviate sparsity but may create local optima when successful behavior initially increases distance from the goal.
3 What are Goals?
Goals in GCRL are task-dependent desired properties represented in forms such as vectors, images, languages, rewards, commands, and skills. The survey also describes how goal-conditioned learning processes behavior goals and relabels historical experience.
- Vector Goals: Goals are desired properties or features of an agent, often represented as vectors for robotics tasks such as pushing, picking, or sliding objects.
- Vector Goals: For vector goals, the goal space is usually a sub-space of the state space, mapped by identity or dimension reduction.
- Image Goals: Images provide a direct representation for complex tasks, while compact latent vectors can encode image observations through models such as VAEs.
- Language Goals: Language goals are human-understandable symbol sequences, commonly expressed as instructions, questions, or semantic predicates.
- Other Forms of Goals: Other goal forms include desired returns and commands specifying a return and horizon, with some approaches learning policies from replay buffers in a supervised style.
- Learning Cycle: A typical GCRL cycle selects or receives a behavior goal, gathers environmental experience, relabels historical data, and then optimizes the policy.
4 Algorithms to GCRL
GCRL algorithms address optimization, sub-goal selection, and relabeling. Their strategies shape rewards, improve exploration and sample efficiency, decompose long-term goals, and reuse replay-buffer experience.
- Optimization: Optimization methods learn policies conditioned on goals, including UVFA, reward shaping, intrinsic rewards, self-imitation, and model-based planning.
- Sub-goal Selection: Sub-goal methods select or generate intermediate targets using difficulty, exploration coverage, past trajectories, learned dynamics, or expert demonstrations.
- Sub-goal Selection: Expert demonstrations can provide waypoint order and support predicting intermediate sub-goals conditioned on the current state.
- Relabeling: Relabeling replaces desired goals in replay-buffer transitions and recomputes rewards before training.
- Relabeling: Hindsight Experience Replay relabels desired goals with achieved goals from the same trajectories, alleviating sparse reward signals and enabling off-policy data reuse.
5 Summary and Future Prospects
The survey reviews GCRL’s development, goal representations, and solution strategies, then identifies future directions including intrinsic skills, offline learning, large decision models, and richer goal formulations.
- Summary: The paper reviews GCRL development and organizes solutions around optimization, sub-goal generation, and relabeling.
- Summary: The survey notes that its discussion does not exhaust GCRL formulations and variants.
- Future Prospects: Future work includes agents exploring possible goals through self-supervised learning rather than relying only on meaningful externally specified goal states.
- Future Prospects: Offline GCRL aims to learn generalized goal-conditioned decision models solely from static datasets without environment interaction.
- Future Prospects: Large decision models could provide general pre-trained goal-conditioned control policies for tasks specified by text or goal images.
- Future Prospects: Additional prospective formulations include time-extended goals, constraint-based goals, and goal composition.