Combining brain-computer interfaces with deep reinforcement learning for robot training: a feasibility study in a simulation environment
Mathias Vukelić,
Michael Bui,
Anna Vorreuther
et al.
Abstract:Deep reinforcement learning (RL) is used as a strategy to teach robot agents how to autonomously learn complex tasks. While sparsity is a natural way to define a reward in realistic robot scenarios, it provides poor learning signals for the agent, thus making the design of good reward functions challenging. To overcome this challenge learning from human feedback through an implicit brain-computer interface (BCI) is used. We combined a BCI with deep RL for robot training in a 3-D physical realistic simulation e… Show more
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