37 search results - page 3 / 8 » Analysis of Inverse Reinforcement Learning with Perturbed De... |
NIPS
2008
13 years 11 months ago
2008
Policy gradient (PG) reinforcement learning algorithms have strong (local) convergence guarantees, but their learning performance is typically limited by a large variance in the e...
ICRA
2010
IEEE
13 years 8 months ago
2010
IEEE
— Reinforcement learning (RL) is one of the most general approaches to learning control. Its applicability to complex motor systems, however, has been largely impossible so far d...
JMLR
2010
13 years 4 months ago
2010
With the goal to generate more scalable algorithms with higher efficiency and fewer open parameters, reinforcement learning (RL) has recently moved towards combining classical tec...
ANOR
2005
13 years 9 months ago
2005
The main objects here are finite-strategy games in which entropic terms are subtracted from the payoffs. After such subtraction each Nash equilibrium solves an explicit, unconstra...
ICML
2003
IEEE
14 years 3 months ago
2003
IEEE
Shaping can be an effective method for improving the learning rate in reinforcement systems. Previously, shaping has been heuristically motivated and implemented. We provide a for...