Markov Models
Regularizing Trajectory Optimization with Denoising Autoencoders
Rinu Boney, Norman Di Palo, Mathias Berglund, Alexander Ilin, Juho Kannala, Antti Rasmus, Harri Valpola
Neural Information Processing Systems http://nips.cc/
4b0eea69deea512c9e2c469187643dc2-Paper-Conference.pdf
Ta s k: Your task is to boil tin.
Learning in Non-Cooperative Configurable Markov Decision Processes Giorgia Ramponi ETH AI Center Zurich, Switzerland gramponi@ethz.ch Alberto Maria Metelli Politecnico di Milano Milan, Italy
Reinforcement Learning agent and a configurator that can modify some environmental parameters to improve the agent's performance.
Inverse Rational Controlwith Partially Observable Continuous Nonlinear Dynamics
Figure crepanc bythe which thee circles which differ .
Learning non-Markovian Decision-Making from State-only Sequences Aoyang Qin,1, 2 Feng Gao 3 Qing Li
While the setting of state-only demonstrations is not common, there are certain exceptions.
Self-OrganizedGroupforCooperativeMulti-agent ReinforcementLearning
The framework of centralized training with decentralized execution (CTDE) [8,28]isone ofthe popular frameworks for solving cooperative multi-agent tasks.
Asynchronous Actor-Critic for Multi-Agent Reinforcement Learning
Temporally-extended actions have been widely used in both learning and planning to improve scalability and reduce complexity.