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.

Mean Field Reinforcement Learning

arXiv:2607. 01525v1 Announce Type: cross Abstract: This monograph provides an introduction to mean field reinforcement learning through the lens of Markov decision processes arising from large-population stochastic control with mean field interactions and common noise.

Hierarchical Reinforcement Learning for Air Combat at DARPA's AlphaDogfight Trials

arXiv:2105. 00990v3 Announce Type: replace Abstract: Autonomous control in high-dimensional, continuous state spaces is a persistent and important challenge in the fields of robotics and artificial intelligence.

From Connectivity to Rewards: Dense Reward Learning with Directed State Graphs

The paper introduces Graph-Guided Quasimetric Dense Reward (G2QDR), a framework that learns a state connectivity model to predict pairwise connectivity strengths in asymmetric environments. These strengths are converted into scalar auxiliary dense rewards, offering continuous guidance across hierarchical levels. G2QDR can be integrated into any existing Goal-Conditioned Hierarchical Reinforcement Learning architecture and shows empirical performance improvements in sparse reward settings with modest computational cost.

Noisy-Space Policy Gradient for Diffusion Policies in Offline Reinforcement Learning

arXiv:2609.06882v1 Announce Type: cross Abstract: Diffusion policies offer a powerful and expressive parameterization for continuous control. Yet, their integration with reinforcement learning remain...

Learning a hierarchy

We’ve developed a hierarchical reinforcement learning algorithm that learns high-level actions useful for solving a range of tasks, allowing fast solving of tasks requiring thousands of timesteps. Our algorithm, when applied to a set of navigation problems, discovers a set of high-level actions for walking and crawling in different directions, which enables the agent to master new navigation tasks quickly.

Entropy-Regularized Reinforcement Learning for Linear-Quadratic Stackelberg Differential Games in Regime-Switching Diffusion Models

arXiv:2606. 28671v1 Announce Type: new Abstract: Stackelberg differential games (SDGs) provide a powerful framework for hierarchical decision-making in stochastic and continuous-time environments, yet their solution remains computationally challenging due to the complexity of traditional dynamic programming and Hamilton-Jacobi-Bellman-Isaacs (HJBI) methods, especially in high-dimensional systems.