Hierarchical Structures and World Models in Goal-Conditioned RL

Current Trends in Goal-Conditioned Reinforcement Learning

The field of goal-conditioned reinforcement learning (RL) is witnessing a surge in innovative approaches aimed at enhancing the efficiency, adaptability, and generalization capabilities of RL agents. A notable trend is the integration of hierarchical structures and temporal constraints into RL algorithms, which allows for more sophisticated task decomposition and execution. This is evident in methods that optimize subgoal generation and enforce temporal ordering constraints, enabling agents to tackle complex, multi-step tasks with improved sample efficiency and reduced non-stationarity.

Another significant development is the exploration of diverse goal selection strategies, which aim to improve the learning of diverse skills by prioritizing goals that enhance discriminability and exploration. These methods often leverage intrinsic motivation and contrastive learning objectives to guide the agent's learning process without relying on extrinsic rewards.

World models are also gaining traction as a means to enhance exploration and planning in RL. These models, trained on offline data, allow agents to predict future states and plan actions without direct environment interaction, thereby improving generalization and reducing the need for extensive online learning.

Noteworthy papers include:

  • Compositional Automata Embeddings for Goal-Conditioned Reinforcement Learning: Demonstrates zero-shot generalization and accelerated policy specialization.
  • Hierarchical Preference Optimization: Shows significant improvement in complex robotic tasks, addressing non-stationarity and infeasible subgoal generation.
  • DINO-WM: World Models on Pre-trained Visual Features: Enables zero-shot planning and strong generalization across diverse domains.

Sources

Compositional Automata Embeddings for Goal-Conditioned Reinforcement Learning

Hierarchical Preference Optimization: Learning to achieve goals via feasible subgoals prediction

Exploring the Edges of Latent State Clusters for Goal-Conditioned Reinforcement Learning

Learning Hidden Subgoals under Temporal Ordering Constraints in Reinforcement Learning

Diversity Progress for Goal Selection in Discriminability-Motivated RL

Show, Don't Tell: Learning Reward Machines from Demonstrations for Reinforcement Learning-Based Cardiac Pacemaker Synthesis

Learning World Models for Unconstrained Goal Navigation

DINO-WM: World Models on Pre-trained Visual Features enable Zero-shot Planning

Few-Shot Task Learning through Inverse Generative Modeling

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