Structured Kernel|Based Reinforcement Learning

Structured Kernel-Based Reinforcement Learning

Abstract. Kernel-based reinforcement learning (KBRL) is a popular ap- proach to learning non-parametric value function approxima-.

Structured Kernel-Based Reinforcement Learning

Kernel-based reinforcement learning (KBRL) is a popular approach to learning non-parametric value function approximations. In this paper, we ...

Kernel-Based Models for Reinforcement Learning

Kernel-based reinforcement learning (KBRL) ... With these two pruning mechanisms, O(log |D|) up- dates are possible by using a data structure such as.

(PDF) Kernel-Based Reinforcement Learning - ResearchGate

We find that all reinforcement learning approaches to estimating the value function, parametric or non-parametric, are subject to a bias. This bias is typically ...

[2404.09760] Effective Reinforcement Learning Based on Structural ...

In this paper, we propose a novel and general Structural Information principles-based framework for effective Decision-Making, namely SIDM, approached from an ...

Kernel-Based Reinforcement Learning: A Finite-Time Analysis

is comparable to other model-based algorithm in structured MDPs (e.g., Jin et al., 2020). The algorithm goes as follows. Assume we are at episode k at step ...

Kernel-Based Reinforcement Learning | Semantic Scholar

Structured Kernel-Based Reinforcement Learning · B. KvetonGeorgios Theocharous. Computer Science. AAAI. 2013. TLDR. This paper presents structured KBRL, a ...

Model-Based Reinforcement Learning:Theory and Practice

Sampling-based planning, in both continuous and discrete domains, can also be combined with structured physics-based, object-centric priors.

Structured State Space Models for In-Context Reinforcement Learning

... based task. We evaluate our modified architecture on a set of partially-observable environments and find that, in practice, our model ...

Open Problem: Order Optimal Regret Bounds for Kernel-Based ...

Proceedings of Thirty Seventh Conference on Learning Theory, PMLR 247:5340-5344, 2024. Abstract. Reinforcement Learning (RL) has shown great empirical success ...

Structure-Based Inverse Reinforcement Learning for Quantification ...

Structure-Based Inverse Reinforcement Learning for Quantification of Biological Knowledge. Abstract: Gene regulatory networks (GRNs) play crucial roles in ...

Practical Kernel-Based Reinforcement Learning

André M.S. Barreto, Doina Precup, Joelle Pineau; 17(67):1−70, 2016. Abstract. Kernel-based reinforcement learning (KBRL) stands out among approximate ...

Reinforcement learning with algorithms from probabilistic structure ...

We use a probabilistic structure estimation procedure based on a likelihood-ratio (LR) test to make a more informed selection of the learning algorithm.

Reinforcement Learning - Nathan Lambert

I spend most of my time thinking about the variant of model-based reinforcement learning, which involves very similar optimizations, but has a structured and ...

How can I apply reinforcement learning to continuous action spaces?

The common way of dealing with this problem is with actor-critic methods. These naturally extend to continuous action spaces.

Beyond sequence: Structure-based machine learning - ScienceDirect

In this review, we look at the variety of structure-based machine learning approaches, how structures can be used as input, and typical applications of these ...

Structured products dynamic hedging based on reinforcement learning

In this article, we propose a reinforcement learning-based model that can help investors dynamically hedge financial products in discrete time using complex ...

Computational evidence for hierarchically structured reinforcement ...

We designed an experiment to test specific predictions of hierarchical RL using a series of subtasks in the realm of context-based learning and ...

Interpretable Preference-based Reinforcement Learning with Tree ...

ACM Reference Format: Tom Bewley and Freddy Lecue. 2022. Interpretable Preference-based Rein- forcement Learning with Tree-Structured Reward Functions. In Proc.

Understanding Model-Based Reinforcement Learning - Medium

Deep learning, on the other hand, is a subset of machine learning focused on algorithms inspired by the structure and function of the brain ...