Transfer Reinforcement Learning
13 papers with code • 0 benchmarks • 1 datasets
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Latest papers
Transfer Reinforcement Learning for Differing Action Spaces via Q-Network Representations
saodem74/Transfer-Learning-in-Reinforcement-Learning
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In this paper, we approach the task of transfer learning between domains that differ in action spaces.
AdaRL: What, Where, and How to Adapt in Transfer Reinforcement Learning
adaptive-rl/adarl-code
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ICLR 2022
We show that by explicitly leveraging this compact representation to encode changes, we can efficiently adapt the policy to the target domain, in which only a few samples are needed and further policy optimization is avoided.
Scalable Multiagent Driving Policies For Reducing Traffic Congestion
Next, we propose a modular transfer reinforcement learning approach, and use it to scale up a multiagent driving policy to outperform human-like traffic and existing approaches in a simulated realistic scenario, which is an order of magnitude larger than past scenarios (hundreds instead of tens of vehicles).
Domain Adaptation In Reinforcement Learning Via Latent Unified State Representation
KarlXing/LUSR
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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.
Action Priors for Large Action Spaces in Robotics
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.
MULTIPOLAR: Multi-Source Policy Aggregation for Transfer Reinforcement Learning between Diverse Environmental Dynamics
Mohammadamin-Barekatain/multipolar
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Transfer reinforcement learning (RL) aims at improving the learning efficiency of an agent by exploiting knowledge from other source agents trained on relevant tasks.
VUSFA:Variational Universal Successor Features Approximator to Improve Transfer DRL for Target Driven Visual Navigation
shamanez/VUSFA-Variational-Universal-Successor-Features-Approximator
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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.
gym-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.
Hardware Conditioned Policies for Multi-Robot Transfer Learning
taochenshh/hcp
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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.
Deep 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.
