A feedback control structure for on-line learning tasks

By Manfred Huber, R. Grupen. 1997

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

Reinforcement learning in robotics: A survey (2013, background): “Huber and Grupen (1997) combined discrete event system and reinforcement learning techniques to learn turning gates for a quadruped.”
Constructing Skill Trees for Reinforcement Learning Agents from Demonstration Trajectories (2010, methodology): “Moreover, with the growing realization that learning policies completely from scratch in such domains is infeasible, we may also need to bootstrap learning through a method that provides a reasonable initial policy such as learning from demonstration [5], sequencing existing controllers [6], using a”
Multi-Level Discovery of Deep Options (2017, background): “…towards discovery of the hierarchical structure itself, by: trading off value with description length [30], identifying transitional states [31, 32, 33, 34, 6], inference from demonstrations [8, 35, 5, 12], iteratively expanding the set of solvable initial states [36, 11], policy gradient [37],…”
Recent Advances in Hierarchical Reinforcement Learning (2003, background): “These partial policies are sometimes called temporally-extended actions, options [73], skills [80], behaviors [9] [27], or the more control-theoretic modes [22].”
SWIRL: A sequential windowed inverse reinforcement learning algorithm for robot tasks with delayed rewards (2018, background): “, 2007; Konidaris and Barto, 2007), a set of waypoints (Kaelbling, 1993), low-level skills (Bacon and Precup, 2015; Huber and Grupen, 1997), a set of finite-state meta-controllers (Parr and Russell, 1997), a set of subgoals (Dietterich, 2000; Sutton et al.”
Intra-Option Learning about Temporally Abstract Actions (1998, background): “Learning, planning, and representing knowledge at multiple levels of temporal abstraction remain key challenges for AI. Recently, several researchers have begun to address these challenges within the framework of reinforcement learning and Markov decision processes (MDPs) (e.g., Singh, 1992a,b; Kael”