Stochastic Neural Networks for hierarchical reinforcement learning

From Ticks to Flows: Dynamics of Neural Reinforcement Learning in Continuous Environments

arXiv:2606. 04275v1 Announce Type: cross Abstract: We present a novel theoretical framework for deep reinforcement learning (RL) in continuous environments by modeling the problem as a continuous-time stochastic process, drawing on insights from stochastic control.

An Introduction to Deep Reinforcement Learning

RL²: Fast reinforcement learning via slow reinforcement learning

Action-Driven Processes for Continuous-Time Control

arXiv:2510.26672v3 Announce Type: replace-cross Abstract: At the heart of reinforcement learning are actions -- decisions made in response to observations of the environment. Actions are equally fund...

Hierarchical Soft Actor-Critic for Sparse-Reward Long-Horizon Reinforcement Learning

arXiv:2607. 23726v1 Announce Type: cross Abstract: Exploration in sparse-reward long-horizon tasks poses significant challenges for reinforcement learning.

Adaptive Mixing of Policies from Searching and Policies from Learning

arXiv:2608. 15700v1 Announce Type: new Abstract: Background: Distillation of training targets generated thru search/planning has proven useful in reinforcement learning, but search can take exceedingly long.