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Found 5 papers, 0 papers with code
Hierarchical Empowerment: Towards Tractable Empowerment-Based Skill Learning
Our framework makes two specific contributions.
Learning robot motor skills with mixed reality
Mixed Reality (MR) has recently shown great success as an intuitive interface for enabling end-users to teach robots.
Does DQN really learn? Exploring adversarial training schemes in Pong
In this work, we study two self-play training schemes, Chainer and Pool, and show they lead to improved agent performance in Atari Pong compared to a standard DQN agent -- trained against the built-in Atari opponent.
Hierarchical Reinforcement Learning of Locomotion Policies in Response to Approaching Objects: A Preliminary Study
Animals such as rabbits and birds can instantly generate locomotion behavior in reaction to a dynamic, approaching object, such as a person or a rock, despite having possibly never seen the object before and having limited perception of the object's properties.
Value-Based Reinforcement Learning for Continuous Control Robotic Manipulation in Multi-Task Sparse Reward Settings
Learning continuous control in high-dimensional sparse reward settings, such as robotic manipulation, is a challenging problem due to the number of samples often required to obtain accurate optimal value and policy estimates.
