Model-based Bayesian reinforcement learning for enhancing primary user performance under jamming attack
… the throughput of a primary user (PU) suffering from a random … an energy-constrained
secondary user (SU) harvesting non-… Bayesian reinforcement learning (BRL) is adopted to learn …
secondary user (SU) harvesting non-… Bayesian reinforcement learning (BRL) is adopted to learn …
A tutorial on Bayesian optimization of expensive cost functions, with application to active user modeling and hierarchical reinforcement learning
E Brochu, VM Cora, N De Freitas - arXiv preprint arXiv:1012.2599, 2010 - arxiv.org
… of Bayesian optimization, with experiments—active user modelling with preferences, and
hierarchical reinforcement learning— and a discussion of the pros and cons of Bayesian …
hierarchical reinforcement learning— and a discussion of the pros and cons of Bayesian …
Bayesian reinforcement learning: A survey
… for internal or personal use, or the internal or personal use of specific … Bayesian methods
for the reinforcement learning (RL) paradigm. The major incentives for incorporating Bayesian …
for the reinforcement learning (RL) paradigm. The major incentives for incorporating Bayesian …
A bayesian reinforcement learning approach for customizing human-robot interfaces
A Atrash, J Pineau - … the 14th international conference on Intelligent user …, 2009 - dl.acm.org
… The particular problem addressed by this paper is the use of learning methods to improve
the … We use a Bayesian reinforcement learning framework, that allows us to mix learning and …
the … We use a Bayesian reinforcement learning framework, that allows us to mix learning and …
Bayesian reinforcement learning-based coalition formation for distributed resource sharing by device-to-device users in heterogeneous cellular networks
A Asheralieva - IEEE Transactions on Wireless …, 2017 - ieeexplore.ieee.org
… This paper presents a novel Bayesian RL-based coalition formation approach for D2D-enabled
heterogeneous networks where a distributed resource allocation problem for D2D users …
heterogeneous networks where a distributed resource allocation problem for D2D users …
Bayesian reinforcement learning for link-level throughput maximization
H Khoshkbari, V Pourahmadi… - IEEE Communications …, 2020 - ieeexplore.ieee.org
… , however, limits their use cases. In this letter, we show how Bayesian RL agents can be used
… we do not consider the user assignment problem and assume that all users in one cell are …
… we do not consider the user assignment problem and assume that all users in one cell are …
Benchmarking for bayesian reinforcement learning
… In this paper, we investigate how the way BRL algorithms use the available computation
time may impact online performances. To properly compare Bayesian algorithms, we designed …
time may impact online performances. To properly compare Bayesian algorithms, we designed …
Exploration in interactive personalized music recommendation: a reinforcement learning approach
… By recommending C several times and gathering user feedback, we will then find out user
4’s … Then, we develop Bayesian models to estimate the posterior distribution of U given the …
4’s … Then, we develop Bayesian models to estimate the posterior distribution of U given the …
Real-world video adaptation with reinforcement learning
… In §3.4 we describe how we use Bayesian optimization to shape the weights for optimizing
… To optimize the multi-dimensional objective, we use a Bayesian Optimization approach for …
… To optimize the multi-dimensional objective, we use a Bayesian Optimization approach for …
Improving the efficiency of Bayesian inverse reinforcement learning
B Michini, JP How - 2012 IEEE International Conference on …, 2012 - ieeexplore.ieee.org
… learning by casting the problem in the Bayesian inference framework. However, the … its use
for even moderate problem sizes. This paper proposes modifications to the original Bayesian …
for even moderate problem sizes. This paper proposes modifications to the original Bayesian …
相关搜索
- bayesian reinforcement learning
- distributed resource sharing bayesian reinforcement
- link level bayesian reinforcement
- bayesian inverse reinforcement
- hierarchical reinforcement tutorial on bayesian optimization
- dynamic bayesian networks human users
- bayesian network user adaptation
- reinforcement learning bayesian sampling approach
- heterogeneous cellular networks bayesian reinforcement
- coalition formation bayesian reinforcement
- user modeling tutorial on bayesian optimization