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Recent advances in reinforcement learning in finance
The rapid changes in the finance industry due to the increasing amount of data have
revolutionized the techniques on data processing and data analysis and brought new …
revolutionized the techniques on data processing and data analysis and brought new …
A survey on model-based reinforcement learning
Reinforcement learning (RL) interacts with the environment to solve sequential decision-
making problems via a trial-and-error approach. Errors are always undesirable in real-world …
making problems via a trial-and-error approach. Errors are always undesirable in real-world …
[ספר][B] Algorithms for decision making
MJ Kochenderfer, TA Wheeler, KH Wray - 2022 - books.google.com
A broad introduction to algorithms for decision making under uncertainty, introducing the
underlying mathematical problem formulations and the algorithms for solving them …
underlying mathematical problem formulations and the algorithms for solving them …
Morel: Model-based offline reinforcement learning
In offline reinforcement learning (RL), the goal is to learn a highly rewarding policy based
solely on a dataset of historical interactions with the environment. This serves as an extreme …
solely on a dataset of historical interactions with the environment. This serves as an extreme …
Reinforcement learning in healthcare: A survey
As a subfield of machine learning, reinforcement learning (RL) aims at optimizing decision
making by using interaction samples of an agent with its environment and the potentially …
making by using interaction samples of an agent with its environment and the potentially …
An introduction to deep reinforcement learning
Deep reinforcement learning is the combination of reinforcement learning (RL) and deep
learning. This field of research has been able to solve a wide range of complex …
learning. This field of research has been able to solve a wide range of complex …
Learning to explore using active neural slam
This work presents a modular and hierarchical approach to learn policies for exploring 3D
environments, calledActive Neural SLAM'. Our approach leverages the strengths of both …
environments, calledActive Neural SLAM'. Our approach leverages the strengths of both …
On the theory of policy gradient methods: Optimality, approximation, and distribution shift
Policy gradient methods are among the most effective methods in challenging reinforcement
learning problems with large state and/or action spaces. However, little is known about even …
learning problems with large state and/or action spaces. However, little is known about even …
Policy finetuning: Bridging sample-efficient offline and online reinforcement learning
Recent theoretical work studies sample-efficient reinforcement learning (RL) extensively in
two settings: learning interactively in the environment (online RL), or learning from an offline …
two settings: learning interactively in the environment (online RL), or learning from an offline …
[ספר][B] Bandit algorithms
T Lattimore, C Szepesvári - 2020 - books.google.com
Decision-making in the face of uncertainty is a significant challenge in machine learning,
and the multi-armed bandit model is a commonly used framework to address it. This …
and the multi-armed bandit model is a commonly used framework to address it. This …