model-based reinforcement learning
Bootstrap Estimated Uncertainty of the Environment Model for Model-Based Reinforcement Learning
... Model-based reinforcement learning (RL) methods attempt to learn a dynamics model to simulate the real environment and utilize the model to make better ...less model error ...
8
Exploration in Relational Domains for Model-based Reinforcement Learning
... The environment of the agent typically contains varying numbers of objects with relations among them. Learning and acting in such large relational domains is a second key challenge in re- inforcement ...
44
Extraversion differentiates between model based and model free strategies in a reinforcement learning task
... the model-free reinforcement strategies tradition- ally associated with error-driven updating are accompanied by an additional system for model-based reinforcement ...a ...
10
Adaptive Policy-based Object Tracking using Reinforcement Learning.
... the model-construction mechanism, object tracking can also be categorized as a generative model-based, discriminative model-based, or hybrid generative-discriminative ...different ...
46
End to end Deep Reinforcement Learning Based Coreference Resolution
... loss based on local decisions rather than the actual coreference evaluation met- rics, while our reinforcement model directly opti- mizes the evaluation metrics based on the rewards calculated ...
6
Use of Reinforcement Learning as a Challenge: A Review
... The model is markov, when the state transitions are independent of any previous states or the agent ...actions. Reinforcement learning is mainly concerned with how an optimal policy can be obtained ...
7
Multi - Agent Collaborative Service Request Scheduling Model Based on Reinforcement Learning
... strategy based on service quality, according to the feedback information in time aware routers overload and request response time and request timeout, using probabilistic methods to reject some random business, ...
7
Deep Reinforcement Learning of the Model Fusion with Double Q learning
... DQN. Based on the double q- learning algorithm we adds different models of neural networks to form a fusion frame module, considering the different neural network structures increase the diversity of ...
7
Practical Kernel-Based Reinforcement Learning
... a model-based algorithm, KBSF is more sample efficient than SARSA, but it is also considerably slower (Atkeson and Santamaria, ...the model and the value function are ...the model starts to ...
70
Intelligent Land Vehicle Model Transfer Trajectory Planning Method Based on Deep Reinforcement Learning
... A model transfer trajectory planning method based on deep reinforcement learning is proposed in this ...abstracted model is transferred into a simple virtual environment through the ...
19
Performance Of Reinforcement Learning Model With Boltzmann Machine For Improving The Intrusion Detection System In Manet
... probability model ensures that identification of malicious nodes from the network and the malicious node is isolated from the ...algorithm based on mathematical intersection principle detect the malicious ...
5
Preana: Game Theory Based Prediction with Reinforcement Learning
... Utility Model is that players cannot look ahead in rounds or even look back and learn from their mistakes or ...Utility Model in each round and then adjusted in each ...a learning matrix and a ...
15
Sentence Simplification with Deep Reinforcement Learning
... a model similar to Ya- mada and Knight (2001) which additionally per- forms simplification-specific rewrite operations ...two-stage model: initially, a standard phrase-based machine transla- tion ...
11
Using Reinforcement Learning to Build a Better Model of Dialogue State
... For our study, we used an annotated corpus of 20 human-computer spoken dialogue tutoring ses- sions. Each session consists of an interaction with one student over 5 different college-level physics problems, for a total ...
8
A Shift Reduce Dependency Parser Based on Reinforcement Learning
... whole model is implemented by ...a learning rate of ...as model parameters, while in this paper, we compare the random initialization method with the pre-training ...
8
DEEP LEARNING ALGORITHM USED IN ROBOTICS
... machine learning with robotics. The canonical model for using deep neural networks for learning a control policy is deep Q-learning ...to model a table of Q- values, which are trained ...
5
Reinforcement learning design for cancer clinical trials
... were based on integrating of IL-2 and interferon-alpha (IFN-α) with the CVD (cisplatin, vinblastine, and dacarbazine) ...mathematical model was made by Kirschner and Panetta ...mathematical model ...
119
Learning to Act with RVRL Agents
... state- based representation, in which every state it encounters is labelled depending on the value of state variables, the number of states is exponential to the number of state ...transition model in a ...
12
Neural Topic Model with Reinforcement Learning
... documents based on the learned latent topic ...inforcement learning and incorporate topic co- herence measures as reward signals to guide the learning of a VAE-based topic ...proposed ...
6
Model-based Bayesian Reinforcement Learning in Factored Markov Decision Process
... systems based on Markov decision process (MDP) or partially observable Markov decision process (POMDP) is an interdisciplinary research area of machine learning, control theory, and operations ...goal. ...
6