A Comparison of Action Spaces for Learning Manipulation Tasks

no code implementations • 23 Aug 2019 • Patrick Varin, Lev Grossman, Scott Kuindersma

Designing reinforcement learning (RL) problems that can produce delicate and precise manipulation policies requires careful choice of the reward function, state, and action spaces.

reinforcement-learning Reinforcement Learning (RL)

Constructing Skill Trees for Reinforcement Learning Agents from Demonstration Trajectories

no code implementations • NeurIPS 2010 • George Konidaris, Scott Kuindersma, Roderic Grupen, Andrew G. Barto

We demonstrate that CST constructs an appropriate skill tree that can be further refined through learning in a challenging continuous domain, and that it can be used to segment demonstration trajectories on a mobile manipulator into chains of skills where each skill is assigned an appropriate abstraction.

reinforcement-learning Reinforcement Learning (RL)