Implementing Online Reinforcement Learning with Temporal Neural Networks

2022-04-11 · James E. Smith

A Temporal Neural Network (TNN) architecture for implementing efficient online reinforcement learning is proposed and studied via simulation. The proposed T-learning system is composed of a frontend TNN that implements online unsupervised clustering and a backend TNN that implements online reinforcement learning. The reinforcement learning paradigm employs biologically plausible neo-Hebbian three-factor learning rules. As a working example, a prototype implementation of the cart-pole problem (balancing an inverted pendulum) is studied via simulation.

📄 PDF Abstract BibTeX arXiv:2204.05437

Code (0)

등록된 구현이 없습니다.

Tasks

Clusteringreinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

Designing and evaluating an online reinforcement learning agent for physical exercise recommendations in N-of-1 trials

2023-09-25 · Dominik Meier, Ipek Ensari, Stefan Konigorski

Personalized adaptive interventions offer the opportunity to increase patient benefits, however, there are challenges in their planning and implementation. Once implemented, it is an important question whether personaliz…

reinforcement-learning Reinforcement Learning

Online Regret Bounds for Undiscounted Continuous Reinforcement Learning

2013-02-11 · NeurIPS 2012 12 · Ronald Ortner, Daniil Ryabko

We derive sublinear regret bounds for undiscounted reinforcement learning in continuous state space. The proposed algorithm combines state aggregation with the use of upper confidence bounds for implementing optimism in …

reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

Guiding Online Reinforcement Learning with Action-Free Offline Pretraining

2023-01-30 · Deyao Zhu, Yuhui Wang, Jürgen Schmidhuber, Mohamed Elhoseiny

Offline RL methods have been shown to reduce the need for environment interaction by training agents using offline collected episodes. However, these methods typically require action information to be logged during data …

Offline RL reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

Diverse Exploration for Fast and Safe Policy Improvement

2018-02-22 · Andrew Cohen, Lei Yu, Robert Wright

We study an important yet under-addressed problem of quickly and safely improving policies in online reinforcement learning domains. As its solution, we propose a novel exploration strategy - diverse exploration (DE), wh…

Diversity reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

Energy-Guided Diffusion Sampling for Offline-to-Online Reinforcement Learning

2024-07-17 · Xu-Hui Liu, Tian-Shuo Liu, Shengyi Jiang, Ruifeng Chen 외

Combining offline and online reinforcement learning (RL) techniques is indeed crucial for achieving efficient and safe learning where data acquisition is expensive. Existing methods replay offline data directly in the on…

MuJoCo reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)