VariBAD: variational bayes-adaptive deep RL via meta-learning

VariBAD: variational bayes-adaptive deep RL via meta-learning

Trading off exploration and exploitation in an unknown environment is key to maximising expected online return during learning. A Bayes-optimal policy, which does so optimally, conditions its actions not only on the environment state but also on the agent's uncertainty about the environment. Co...

Deskribapen osoa

Xehetasun bibliografikoak
Egile nagusia:	Whiteson, S
Formatua:	Journal article
Hizkuntza:	English
Argitaratua:	Journal of Machine Learning Research 2021

Antzeko izenburuak

VariBAD: a very good method for Bayes-adaptive deep RL via meta-learning
nork: Zintgraf, L, et al.
Argitaratua: (2020)

Knowledge Transfer in Deep Reinforcement Learning via an RL-Specific GAN-Based Correspondence Function
nork: Marko Ruman, et al.
Argitaratua: (2024-01-01)

PharmRL: pharmacophore elucidation with deep geometric reinforcement learning
nork: Rishal Aggarwal, et al.
Argitaratua: (2024-12-01)

Fast Context Adaptation via Meta-Learning
nork: Zintgraf, L, et al.
Argitaratua: (2019)

Experience Replay Optimisation via ATSC and TSC for Performance Stability in Deep RL
nork: Richard Sakyi Osei, et al.
Argitaratua: (2023-02-01)

AC-RL: A Framework for Real-Time Control, Learning & Adaptation
nork: Guha, Anubhav
Argitaratua: (2023)

Learning to Utilize Curiosity: A New Approach of Automatic Curriculum Learning for Deep RL
nork: Zeyang Lin, et al.
Argitaratua: (2022-07-01)

RobustAnalog: Fast Variation-Aware Analog Circuit Design Via Multi-task RL
nork: Shi, Wei, et al.
Argitaratua: (2022)

Fiber Bundle Meta-learning Algorithm Based on Variational Bayes
nork: LIU Yang, LI Fan-zhang
Argitaratua: (2022-03-01)

Reinforcement learning (RL) based stock trading system via support vector machine
nork: Ong, Zhi Yuan.
Argitaratua: (2010)

GenFedRL: a general federated reinforcement learning framework for deep reinforcement learning agents
nork: Biao JIN, et al.
Argitaratua: (2023-06-01)

GenFedRL: a general federated reinforcement learning framework for deep reinforcement learning agents
nork: Biao JIN, et al.
Argitaratua: (2023-06-01)

Time-in-action RL
nork: Jiangcheng Zhu, et al.
Argitaratua: (2019-02-01)

RL-ANC: Reinforcement Learning-Based Adaptive Network Coding in the Ocean Mobile Internet of Things
nork: Ying Zhang, et al.
Argitaratua: (2024-06-01)

RL-AKF: An Adaptive Kalman Filter Navigation Algorithm Based on Reinforcement Learning for Ground Vehicles
nork: Xile Gao, et al.
Argitaratua: (2020-05-01)

Exploiting multiple abstractions in episodic RL via reward shaping
nork: Cipollone, R, et al.
Argitaratua: (2023)

Deep variational reinforcement learning for POMDPs
nork: Igl, M, et al.
Argitaratua: (2018)

rl4dtn: Q-Learning for Opportunistic Networks
nork: Jorge Visca, et al.
Argitaratua: (2022-11-01)

ACC-RL: Adaptive Congestion Control Based on Reinforcement Learning in Power Distribution Networks with Data Centers
nork: Tairan Huang, et al.
Argitaratua: (2023-07-01)

RL-SPIHT: Reinforcement Learning-Based Adaptive Selection of Compression Ratios for 1-D SPIHT Algorithm
nork: Jin Shin, et al.
Argitaratua: (2021-01-01)

RL-QPSO net: deep reinforcement learning-enhanced QPSO for efficient mobile robot path planning
nork: Yang Jing, et al.
Argitaratua: (2025-01-01)

iADA*-RL: Anytime Graph-Based Path Planning with Deep Reinforcement Learning for an Autonomous UAV
nork: Aye Aye Maw, et al.
Argitaratua: (2021-04-01)

Reflections of RL in The Virtual World
nork: Andra Siibak
Argitaratua: (2007-11-01)

Elimination of All Bad Local Minima in Deep Learning
nork: Kawaguchi, Kenji, et al.
Argitaratua: (2021)

Automation of digital crime investigation using Reinforcement Learning (RL)
nork: Ghanem, Mohamed Chahine
Argitaratua: (2023)

RL4CEP: reinforcement learning for updating CEP rules
nork: Afef Mdhaffar, et al.
Argitaratua: (2025-01-01)

CST-RL: Contrastive Spatio-Temporal Representations for Reinforcement Learning
nork: Chi-Kai Ho, et al.
Argitaratua: (2023-01-01)

ADAS-RL: Safety learning approach for stable autonomous driving
nork: Dongsu Lee, et al.
Argitaratua: (2022-09-01)

HLifeRL: A hierarchical lifelong reinforcement learning framework
nork: Fan Ding, et al.
Argitaratua: (2022-07-01)

RL-CWtrans Net: multimodal swimming coaching driven via robot vision
nork: Guanlin Wang
Argitaratua: (2024-08-01)

Improving Student Learning Outcomes Through the TaRL Learning Model on Discussion
nork: Miftahunajah Aditiya Pratama
Argitaratua: (2023-11-01)

Implementation of the TaRL Approach to Increase Student Learning Motivation in Physics Learning
nork: Melinda Cahya Ningrum Ningrum, et al.
Argitaratua: (2023-05-01)

Model-based RL in ATARI games
nork: Akarapu, Bharadwaj
Argitaratua: (2021)

Information asymmetry in KL-regularized RL
nork: Galashov, A, et al.
Argitaratua: (2018)

Model-Free RL or Action Sequences?
nork: Adam Morris, et al.
Argitaratua: (2019-12-01)

R.L. Moore : mathematician and teacher /
nork: 236772 Parker, John
Argitaratua: (2005)

Packet Size-Aware Broadcasting in VANETs With Fuzzy Logic and RL-Based Parameter Adaptation
nork: Celimuge Wu, et al.
Argitaratua: (2015-01-01)

RAMBO-RL: robust adversarial model-based offline reinforcement learning
nork: Rigter, M, et al.
Argitaratua: (2023)

SpaceRL — A reinforcement learning-based knowledge graph driver
nork: Miguel Bermudo, et al.
Argitaratua: (2025-05-01)

FleetRL: Realistic reinforcement learning environments for commercial vehicle fleets
nork: Enzo Cording, et al.
Argitaratua: (2024-05-01)