research
∙
06/16/2023
Dynamic Size Message Scheduling for Multi-Agent Communication under Limited Bandwidth
Communication plays a vital role in multi-agent systems, fostering colla...
share
research
∙
01/30/2023
Transferring Multiple Policies to Hotstart Reinforcement Learning in an Air Compressor Management Problem
Many instances of similar or almost-identical industrial machines or too...
research
∙
06/10/2021
Synthesising Reinforcement Learning Policies through Set-Valued Inductive Rule Learning
Today's advanced Reinforcement Learning algorithms produce black-box pol...
research
∙
07/18/2019
Transfer Learning Across Simulated Robots With Different Sensors
For a robot to learn a good policy, it often requires expensive equipmen...
research
∙
03/11/2019
Sample-Efficient Model-Free Reinforcement Learning with Off-Policy Critics
Value-based reinforcement-learning algorithms are currently state-of-the...
research
∙
02/07/2019
The Actor-Advisor: Policy Gradient With Off-Policy Advice
Actor-critic algorithms learn an explicit policy (actor), and an accompa...
research
∙
09/20/2018
Dynamic Weights in Multi-Objective Deep Reinforcement Learning
Many real-world decision problems are characterized by multiple objectiv...
research
∙
08/13/2018
Directed Policy Gradient for Safe Reinforcement Learning with Human Advice
Many currently deployed Reinforcement Learning agents work in an environ...
research
∙
08/22/2017
Reinforcement Learning in POMDPs with Memoryless Options and Option-Observation Initiation Sets
Many real-world reinforcement learning problems have a hierarchical natu...
research
∙
12/17/2015