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.
HySRL improves RL sample efficiency with shifted-dynamics data.
Hybrid RL algorithms improve offline and online RL in linear MDPs.
Paper proposes a hybrid RL algorithm that combines offline and online data without needing reward info.
New algorithm fills gaps in offline data for hybrid RL, achieving similar gains without coverage assumptions.
Hybrid RL algorithm combines offline and online data for robust and efficient policy learning.
Unified hybrid RL algorithm improves online RL performance with offline data.
SF-DQN improves RL transfer by learning successor features.
New hybrid RL algorithm outperforms model-free and model-based methods.
Q-learning is one of the most popular methods in Reinforcement Learning (RL). Transfer Learning aims to utilize the learned knowledge from source tasks to help new tasks to improve the sample complexity of the new tasks. Considering that data collection in RL is both more time and cost consuming and Q-learning converge…
We propose a probabilistic framework to directly insert prior knowledge in reinforcement learning (RL) algorithms by defining the behaviour policy as a Bayesian posterior distribution. Such a posterior combines task specific information with prior knowledge, thus allowing to achieve transfer learning across tasks. The …
Time-variant value function transfer method for RL.
We consider the problem of knowledge transfer when an agent is facing a series of Reinforcement Learning (RL) tasks. We introduce a novel metric between Markov Decision Processes (MDPs) and establish that close MDPs have close optimal value functions. Formally, the optimal value functions are Lipschitz continuous with …
Current reinforcement learning (RL) methods can successfully learn single tasks but often generalize poorly to modest perturbations in task domain or training procedure. In this work, we present a decoupled learning strategy for RL that creates a shared representation space where knowledge can be robustly transferred. …
Reincarnating RL reuses prior work to accelerate RL progress.
Transfer Learning (TL) has shown great potential to accelerate Reinforcement Learning (RL) by leveraging prior knowledge from past learned policies of relevant tasks. Existing transfer approaches either explicitly computes the similarity between tasks or select appropriate source policies to provide guided explorations…
One-step Bellman alignment improves online RL by reducing task mismatch.
We extend the concept of transfer learning, widely applied in modern machine learning algorithms, to the emerging context of hybrid neural networks composed of classical and quantum elements. We propose different implementations of hybrid transfer learning, but we focus mainly on the paradigm in which a pre-trained cla…
Proposes a method to improve few-shot transfer in off-dynamics RL.
Paper introduces a new method for improving reinforcement learning performance using transfer learning.
Hybrid framework optimizes reinsurance using generative models and reinforcement learning.
Pre-trained language model boosts RL efficiency.
Hybrid RL method optimizes trading by balancing continuous and discrete actions.
In recent years, supervised machine learning models have demonstrated tremendous success in a variety of application domains. Despite the promising results, these successful models are data hungry and their performance relies heavily on the size of training data. However, in many healthcare applications it is difficult…
Offline RL tackles resource-constrained online deployment with improved policy transfer.
While most current research in Reinforcement Learning (RL) focuses on improving the performance of the algorithms in controlled environments, the use of RL under constraints like those met in the video game industry is rarely studied. Operating under such constraints, we propose Hybrid SAC, an extension of the Soft Act…
Transfer learning across different reinforcement learning (RL) tasks is becoming an increasingly valuable area of research. We consider a goal-based multi-task RL framework and mechanisms by which previously solved tasks can reduce sample complexity and regret when the agent is faced with a new task. Specifically, we i…
We consider the transfer of experience samples (i.e., tuples < s, a, s', r >) in reinforcement learning (RL), collected from a set of source tasks to improve the learning process in a given target task. Most of the related approaches focus on selecting the most relevant source samples for solving the target task, but t…
In this report, we present a new reinforcement learning (RL) benchmark based on the Sonic the Hedgehog (TM) video game franchise. This benchmark is intended to measure the performance of transfer learning and few-shot learning algorithms in the RL domain. We also present and evaluate some baseline algorithms on the new…
New algorithm uses density ratios for efficient online reinforcement learning.
Framework for lifelong learning using eigentasks to avoid forgetting and transfer knowledge.
Safe RL-based vibration control using LQR guidance.
We introduce SLM Lab, a software framework for reproducible reinforcement learning (RL) research. SLM Lab implements a number of popular RL algorithms, provides synchronous and asynchronous parallel experiment execution, hyperparameter search, and result analysis. RL algorithms in SLM Lab are implemented in a modular w…
New framework for RL transfer learning with state-action mismatch.
Transfer in Reinforcement Learning (RL) refers to the idea of applying knowledge gained from previous tasks to solving related tasks. Learning a universal value function (Schaul et al., 2015), which generalizes over goals and states, has previously been shown to be useful for transfer. However, successor features are b…
In recent years, Multifactorial Optimization (MFO) has gained a notable momentum in the research community. MFO is known for its inherent capability to efficiently address multiple optimization tasks at the same time, while transferring information among such tasks to improve their convergence speed. On the other hand,…
UCB-TQL learns from multiple tasks with shared dynamics and adapts to task-specific variations.
Consider mutli-goal tasks that involve static environments and dynamic goals. Examples of such tasks, such as goal-directed navigation and pick-and-place in robotics, abound. Two types of Reinforcement Learning (RL) algorithms are used for such tasks: model-free or model-based. Each of these approaches has limitations.…
Taxonomy of knowledge modalities in RL for better transfer.
This paper combines LLMs with RL for better trading strategies.
Paper tackles sim-to-real transfer in continuous domains with partial observations.
Study proposes adaptive RL for dynamic portfolio optimization.
Paper presents a hybrid framework combining sentiment analysis and market indicators for financial portfolio optimization.
Paper analyzes AIRL in high-dimensional spaces using random matrix theory.
WAPPO optimizes feature distributions for better visual transfer in RL.
Paper tackles robust knowledge transfer in parallel RL tasks.
Transfer and adaptation to new unknown environmental dynamics is a key challenge for reinforcement learning (RL). An even greater challenge is performing near-optimally in a single attempt at test time, possibly without access to dense rewards, which is not addressed by current methods that require multiple experience …
The composition of elementary behaviors to solve challenging transfer learning problems is one of the key elements in building intelligent machines. To date, there has been plenty of work on learning task-specific policies or skills but almost no focus on composing necessary, task-agnostic skills to find a solution to …
Study shows how to motivate AI agents like humans through learning from demonstrations.