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POLICY ADVICE, NON-CONVEX AND DISTRIBUTED OPTIMIZATION IN REINFORCEMENT LEARNING
Transfer learning is a method in machine learning that tries to use previous training knowledge to speed up the learning process. Policy advice is a type of transfer learning method where a student agent is able to learn ...
Learning from Human Teachers: Supporting How People Want to Teach in Interactive Machine Learning
As the number of deployed robots grows, there will be an increasing need for humans to teach robots new skills that were not pre-programmed, without requiring these users to have any experience with programming or artificial ...
Knowledge Transfer in Reinforcement Learning: How agents should benefit from prior knowledge
Reinforcement learning (RL) has had many successes in different tasks, but in practice, it often requires significant amounts of data or training time to learn high-performing policies. For complicated tasks, the learning ...