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Found 6 papers, 1 papers with code
Transferring Multiple Policies to Hotstart Reinforcement Learning in an Air Compressor Management Problem
Many instances of similar or almost-identical industrial machines or tools are often deployed at once, or in quick succession.
Transfer Learning Across Simulated Robots With Different Sensors
After training in the lab, the robot should be able to get by without the expensive equipment that used to be available to it, and yet still be guaranteed to perform well on the field.
Sample-Efficient Model-Free Reinforcement Learning with Off-Policy Critics
We argue that actor-critic algorithms are limited by their need for an on-policy critic.
The Actor-Advisor: Policy Gradient With Off-Policy Advice
In this paper, we propose an elegant solution, the Actor-Advisor architecture, in which a Policy Gradient actor learns from unbiased Monte-Carlo returns, while being shaped (or advised) by the Softmax policy arising from an off-policy critic.
Directed Policy Gradient for Safe Reinforcement Learning with Human Advice
Our technique, Directed Policy Gradient (DPG), allows a teacher or backup policy to override the agent before it acts undesirably, while allowing the agent to leverage human advice or directives to learn faster.
Reinforcement Learning in POMDPs with Memoryless Options and Option-Observation Initiation Sets
Many real-world reinforcement learning problems have a hierarchical nature, and often exhibit some degree of partial observability.
