A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Curious hierarchical reinforcement learning improves learning performance.
Develops hierarchical reinforcement learning value function approximators.
Novel online algorithm for hierarchical imitation learning.
Free Random Projection enhances reinforcement learning by naturally incorporating hierarchical structure.
Proves efficient learning of hierarchical structure in meta-reinforcement learning.
HAL learns hierarchical affordances to prune impossible subtasks, improving reinforcement learning efficiency.
A novel framework for adaptive multi-agent communication in reinforcement learning.
We study how to effectively leverage expert feedback to learn sequential decision-making policies. We focus on problems with sparse rewards and long time horizons, which typically pose significant challenges in reinforcement learning. We propose an algorithmic framework, called hierarchical guidance, that leverages the…
Graph Pointer Networks and hierarchical reinforcement learning solve combinatorial optimization problems like TSP.
Paper tackles RL for power grid topology optimization.
Hierarchical reinforcement learning has demonstrated significant success at solving difficult reinforcement learning (RL) tasks. Previous works have motivated the use of hierarchy by appealing to a number of intuitive benefits, including learning over temporally extended transitions, exploring over temporally extended …
The paper introduces an adjacency constraint to improve goal-conditioned HRL.
Real-world tasks are often highly structured. Hierarchical reinforcement learning (HRL) has attracted research interest as an approach for leveraging the hierarchical structure of a given task in reinforcement learning (RL). However, identifying the hierarchical policy structure that enhances the performance of RL is n…
Proves EM algorithm guarantees for hierarchical imitation learning.
Eikonal-Constrained QRL improves goal-reaching in reinforcement learning.
Paper shows intrinsic motivation boosts exploration efficiency in HRL.
A novel framework combines LLMs and RL for financial portfolio optimization.
E-learning systems are capable of providing more adaptive and efficient learning experiences for students than the traditional classroom setting. A key component of such systems is the learning strategy, the algorithm that designs the learning paths for students based on information such as the students' current progre…
In this work, we provide theoretical guarantees for reward decomposition in deterministic MDPs. Reward decomposition is a special case of Hierarchical Reinforcement Learning, that allows one to learn many policies in parallel and combine them into a composite solution. Our approach builds on mapping this problem into a…
Global convergence proved for multi-agent LQRs with hierarchical actor-critic.
In hierarchical reinforcement learning a major challenge is determining appropriate low-level policies. We propose an unsupervised learning scheme, based on asymmetric self-play from Sukhbaatar et al. (2018), that automatically learns a good representation of sub-goals in the environment and a low-level policy that can…
PALM learns abstract models for efficient planning and task transfer.
TradeR uses RL to execute trades in real markets, minimizing surprise and catastrophe.
In this work, we take a representation learning perspective on hierarchical reinforcement learning, where the problem of learning lower layers in a hierarchy is transformed into the problem of learning trajectory-level generative models. We show that we can learn continuous latent representations of trajectories, which…
Reinforcement learning and symbolic planning have both been used to build intelligent autonomous agents. Reinforcement learning relies on learning from interactions with real world, which often requires an unfeasibly large amount of experience. Symbolic planning relies on manually crafted symbolic knowledge, which may …
AIRL learns robust, generalizable reward functions from demonstrations.
Extends deep learning for hierarchical data to improve classification accuracy.
Reinforcement learning agents that operate in diverse and complex environments can benefit from the structured decomposition of their behavior. Often, this is addressed in the context of hierarchical reinforcement learning, where the aim is to decompose a policy into lower-level primitives or options, and a higher-leve…
Learning goal-directed behavior in environments with sparse feedback is a major challenge for reinforcement learning algorithms. The primary difficulty arises due to insufficient exploration, resulting in an agent being unable to learn robust value functions. Intrinsically motivated agents can explore new behavior for …
One of the key challenges in applying reinforcement learning to real-life problems is that the amount of train-and-error required to learn a good policy increases drastically as the task becomes complex. One potential solution to this problem is to combine reinforcement learning with automated symbol planning and utili…
Unified pair trading approach using hierarchical reinforcement learning.
New method automates asymmetric choice for better skill transfer in reinforcement learning.
Open-domain dialog generation is a challenging problem; maximum likelihood training can lead to repetitive outputs, models have difficulty tracking long-term conversational goals, and training on standard movie or online datasets may lead to the generation of inappropriate, biased, or offensive text. Reinforcement Lear…
HTMRL uses HTM for RL, adapting faster to changing environments.
Machine learning pipeline potentially consists of several stages of operations like data preprocessing, feature engineering and machine learning model training. Each operation has a set of hyper-parameters, which can become irrelevant for the pipeline when the operation is not selected. This gives rise to a hierarchica…
Hierarchical reinforcement learning is a promising approach to tackle long-horizon decision-making problems with sparse rewards. Unfortunately, most methods still decouple the lower-level skill acquisition process and the training of a higher level that controls the skills in a new task. Leaving the skills fixed can le…
Hierarchical MARL learns complementary skills for team coordination.
Director learns hierarchical behaviors from pixels, outperforming exploration methods.
In this study, we investigate the use of global information to speed up the learning process and increase the cumulative rewards of reinforcement learning (RL) in competition tasks. Within the actor-critic RL, we introduce multiple cooperative critics from two levels of the hierarchy and propose a reinforcement learnin…
EarnHFT tackles HFT challenges with hierarchical RL, significantly outperforming existing methods.
From a young age humans learn to use grammatical principles to hierarchically combine words into sentences. Action grammars is the parallel idea, that there is an underlying set of rules (a "grammar") that govern how we hierarchically combine actions to form new, more complex actions. We introduce the Action Grammar Re…
Method solves long-horizon robotic tasks via imitation and reinforcement learning.
HiDe learns hierarchical control for complex tasks by separating planning and control.
This study compares three portfolio optimization methods on Indian stocks.
Paper presents a hybrid framework combining sentiment analysis and market indicators for financial portfolio optimization.
Nonlinear optimal control problems are often solved with numerical methods that require knowledge of system's dynamics which may be difficult to infer, and that carry a large computational cost associated with iterative calculations. We present a novel neurobiologically inspired hierarchical learning framework, Reinfor…
AI learns to design chemical processes efficiently.
DeepLine automates ML pipeline generation using reinforcement learning.