Papers Transfer Reinforcement Learning
“Transfer Reinforcement Learning” 태그가 달린 논문 41편 · 필터 해제
Learn Dynamic-Aware State Embedding for Transfer Learning
Transfer reinforcement learning aims to improve the sample efficiency of solving unseen new tasks by leveraging experiences obtained from previous tasks. We consider the setting where all tasks (MDPs) share the same envi…
Transfer LearningTransfer Reinforcement LearningModular Transfer Learning with Transition Mismatch Compensation for Excessive Disturbance Rejection
Underwater robots in shallow waters usually suffer from strong wave forces, which may frequently exceed robot's control constraints. Learning-based controllers are suitable for disturbance rejection control, but the exce…
Transfer LearningTransfer Reinforcement LearningAdaptive Energy Management for Real Driving Conditions via Transfer Reinforcement Learning
This article proposes a transfer reinforcement learning (RL) based adaptive energy managing approach for a hybrid electric vehicle (HEV) with parallel topology. This approach is bi-level. The up-level characterizes how t…
energy managementManagementreinforcement-learningReinforcement Learning+2Transfer Reinforcement Learning under Unobserved Contextual Information
In this paper, we study a transfer reinforcement learning problem where the state transitions and rewards are affected by the environmental context. Specifically, we consider a demonstrator agent that has access to a con…
Motion PlanningQ-Learningreinforcement-learningReinforcement Learning+2Universal Successor Features for Transfer Reinforcement Learning
Transfer in Reinforcement Learning (RL) refers to the idea of applying knowledge gained from previous tasks to solving related tasks. Learning a universal value function (Schaul et al., 2015), which generalizes over goal…
MuJoCoreinforcement-learningReinforcement LearningReinforcement Learning (RL)+1How Does an Approximate Model Help in Reinforcement Learning?
One of the key approaches to save samples in reinforcement learning (RL) is to use knowledge from an approximate model such as its simulator. However, how much does an approximate model help to learn a near-optimal polic…
reinforcement-learningReinforcement LearningReinforcement Learning (RL)Transfer Learning+1Federated Transfer Reinforcement Learning for Autonomous Driving
Reinforcement learning (RL) is widely used in autonomous driving tasks and training RL models typically involves in a multi-step process: pre-training RL models on simulators, uploading the pre-trained model to real-life…
Autonomous DrivingCollision Avoidancereinforcement-learningReinforcement Learning+2MULTIPOLAR: Multi-Source Policy Aggregation for Transfer Reinforcement Learning between Diverse Environmental Dynamics
Transfer reinforcement learning (RL) aims at improving the learning efficiency of an agent by exploiting knowledge from other source agents trained on relevant tasks. However, it remains challenging to transfer knowledge…
Reinforcement LearningReinforcement Learning (RL)Transfer Reinforcement LearningGeneralization in Transfer Learning
Agents trained with deep reinforcement learning algorithms are capable of performing highly complex tasks including locomotion in continuous environments. We investigate transferring the learning acquired in one task to …
continuous-controlContinuous ControlDeep Reinforcement LearningFriction+5VUSFA:Variational Universal Successor Features Approximator to Improve Transfer DRL for Target Driven Visual Navigation
In this paper, we show how novel transfer reinforcement learning techniques can be applied to the complex task of target driven navigation using the photorealistic AI2THOR simulator. Specifically, we build on the concept…
reinforcement-learningReinforcement LearningReinforcement Learning (RL)Transfer Learning+2gym-gazebo2, a toolkit for reinforcement learning using ROS 2 and Gazebo
This paper presents an upgraded, real world application oriented version of gym-gazebo, the Robot Operating System (ROS) and Gazebo based Reinforcement Learning (RL) toolkit, which complies with OpenAI Gym. The content d…
BenchmarkingOpenAI Gymreinforcement-learningReinforcement Learning+2Hardware Conditioned Policies for Multi-Robot Transfer Learning
Deep reinforcement learning could be used to learn dexterous robotic policies but it is challenging to transfer them to new robots with vastly different hardware properties. It is also prohibitively expensive to learn a …
Deep Reinforcement LearningIndustrial RobotsReinforcement LearningTransfer Learning+1Deep Transfer Reinforcement Learning for Text Summarization
Deep neural networks are data hungry models and thus face difficulties when attempting to train on small text datasets. Transfer learning is a potential solution but their effectiveness in the text domain is not as explo…
reinforcement-learningReinforcement LearningReinforcement Learning (RL)Text Summarization+2Bayesian Transfer Reinforcement Learning with Prior Knowledge Rules
We propose a probabilistic framework to directly insert prior knowledge in reinforcement learning (RL) algorithms by defining the behaviour policy as a Bayesian posterior distribution. Such a posterior combines task spec…
reinforcement-learningReinforcement LearningReinforcement Learning (RL)Transfer Learning+1VPE: Variational Policy Embedding for Transfer Reinforcement Learning
Reinforcement Learning methods are capable of solving complex problems, but resulting policies might perform poorly in environments that are even slightly different. In robotics especially, training and deployment condit…
reinforcement-learningReinforcement LearningReinforcement Learning (RL)Transfer Reinforcement LearningUniversal Successor Representations for Transfer Reinforcement Learning
The objective of transfer reinforcement learning is to generalize from a set of previous tasks to unseen new tasks. In this work, we focus on the transfer scenario where the dynamics among tasks are the same, but their g…
reinforcement-learningReinforcement LearningReinforcement Learning (RL)Transfer Learning+1Universal Planning Networks
A key challenge in complex visuomotor control is learning abstract representations that are effective for specifying goals, planning, and generalization. To this end, we introduce universal planning networks (UPN). UPNs …
Imitation LearningReinforcement LearningRepresentation LearningTransfer Reinforcement LearningShapechanger: Environments for Transfer Learning
We present Shapechanger, a library for transfer reinforcement learning specifically designed for robotic tasks. We consider three types of knowledge transfer---from simulation to simulation, from simulation to real, and …
reinforcement-learningReinforcement LearningReinforcement Learning (RL)Transfer Learning+1Personalizing a Dialogue System with Transfer Reinforcement Learning
It is difficult to train a personalized task-oriented dialogue system because the data collected from each individual is often insufficient. Personalized dialogue systems trained on a small dataset can overfit and make i…
reinforcement-learningReinforcement LearningReinforcement Learning (RL)Transfer Learning+1Actor-Mimic: Deep Multitask and Transfer Reinforcement Learning
The ability to act in multiple environments and transfer previous knowledge to new situations can be considered a critical aspect of any intelligent agent. Towards this goal, we define a novel method of multitask and tra…
Atari GamesDeep Reinforcement LearningModel Compressionreinforcement-learning+4