Transfer Reinforcement Learning

13 papers with code • 0 benchmarks • 0 datasets

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Most implemented papers

Actor-Mimic: Deep Multitask and Transfer Reinforcement Learning

eparisotto/ActorMimic 19 Nov 2015

The ability to act in multiple environments and transfer previous knowledge to new situations can be considered a critical aspect of any intelligent agent.

MULTIPOLAR: Multi-Source Policy Aggregation for Transfer Reinforcement Learning between Diverse Environmental Dynamics

Mohammadamin-Barekatain/multipolar 28 Sep 2019

Transfer reinforcement learning (RL) aims at improving the learning efficiency of an agent by exploiting knowledge from other source agents trained on relevant tasks.

Shapechanger: Environments for Transfer Learning

seba-1511/shapechanger 15 Sep 2017

We present Shapechanger, a library for transfer reinforcement learning specifically designed for robotic tasks.

Universal Planning Networks

aravindsrinivas/upn 2 Apr 2018

We find that the representations learned are not only effective for goal-directed visual imitation via gradient-based trajectory optimization, but can also provide a metric for specifying goals using images.

Deep Transfer Reinforcement Learning for Text Summarization

yaserkl/TransferRL 15 Oct 2018

Deep neural networks are data hungry models and thus face difficulties when attempting to train on small text datasets.

Hardware Conditioned Policies for Multi-Robot Transfer Learning

taochenshh/hcp NeurIPS 2018

In tasks where knowing the agent dynamics is important for success, we learn an embedding for robot hardware and show that policies conditioned on the encoding of hardware tend to generalize and transfer well.

gym-gazebo2, a toolkit for reinforcement learning using ROS 2 and Gazebo

AcutronicRobotics/gym-gazebo2 14 Mar 2019

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.

VUSFA:Variational Universal Successor Features Approximator to Improve Transfer DRL for Target Driven Visual Navigation

shamanez/VUSFA-Variational-Universal-Successor-Features-Approximator 18 Aug 2019

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.

Action Priors for Large Action Spaces in Robotics

ondrejba/action_priors 11 Jan 2021

This paper proposes an alternative approach where the solutions of previously solved tasks are used to produce an action prior that can facilitate exploration in future tasks.

Domain Adaptation In Reinforcement Learning Via Latent Unified State Representation

KarlXing/LUSR 10 Feb 2021

To address this issue, we propose a two-stage RL agent that first learns a latent unified state representation (LUSR) which is consistent across multiple domains in the first stage, and then do RL training in one source domain based on LUSR in the second stage.