arXiv AI
Topology-Guided Modular Actor-Critic Learning for Continuous Systems under Temporal Objectives
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
Related stories
arXiv AI
Jun 6
From Kinematics to Dynamics: Learning to Refine Hybrid Plans for Physically Feasible Execution
arXiv:2604. 12474v3 Announce Type: replace-cross Abstract: In many robotic tasks, agents must traverse a sequence of spatial regions to complete a mission.
arXiv AI
Aug 14
Exploiting Symbolic Heuristics for the Synthesis of Domain-Specific Temporal Planning Guidance using Reinforcement Learning
arXiv:2505. 13372v2 Announce Type: replace Abstract: Recent work investigated the use of Reinforcement Learning (RL) for the synthesis of heuristic guidance to improve the performance of temporal planners when a domain is fixed and a set of training problems (not plans) is given.
arXiv Machine Learning
Jun 9
Latent Spherical Flow Policy for Reinforcement Learning with Combinatorial Actions
arXiv:2601. 22211v2 Announce Type: replace Abstract: Reinforcement learning (RL) with combinatorial action spaces remains challenging because feasible action sets are exponentially large and governed by complex feasibility constraints, making direct policy parameterization impractical.
arXiv Machine Learning
Sep 1
Locally-Guided Actor-Critic: Training a Goal-conditioned Actor with a Subgoal-aware Critic
arXiv:2608.30406v1 Announce Type: new Abstract: Goal-conditioned reinforcement learning struggles with long horizons when rewards are sparse. While a planner can provide subgoals to guide a low-level...
arXiv Machine Learning
Jun 8
Latent Geometry Beyond Search: Amortizing Planning in World Models
arXiv:2605. 08732v2 Announce Type: replace-cross Abstract: Modern vision-based world models can represent observations as compact yet expressive latent manifolds, but fast goal-oriented planning in these spaces remains challenging.
arXiv AI
Jun 8
CHDP: Cooperative Hybrid Diffusion Policies for Reinforcement Learning in Parameterized Action Space
arXiv:2601. 05675v2 Announce Type: replace Abstract: Hybrid action space, which combines discrete choices and continuous parameters, is prevalent in domains such as robot control and game AI.