Probabilistic Planning with Sequential Monte Carlo methods

[1]  Fabio Viola,et al.  Learning and Querying Fast Generative Models for Reinforcement Learning , 2018, ArXiv.

[2]  Sergey Levine,et al.  Neural Network Dynamics for Model-Based Deep Reinforcement Learning with Model-Free Fine-Tuning , 2017, 2018 IEEE International Conference on Robotics and Automation (ICRA).

[3]  Demis Hassabis,et al.  Mastering the game of Go without human knowledge , 2017, Nature.

[4]  Dale Schuurmans,et al.  Bridging the Gap Between Value and Policy Based Reinforcement Learning , 2017, NIPS.

[5]  Sergey Levine,et al.  Deep visual foresight for planning robot motion , 2016, 2017 IEEE International Conference on Robotics and Automation (ICRA).

[6]  Richard E. Turner,et al.  Neural Adaptive Sequential Monte Carlo , 2015, NIPS.

[7]  Fredrik Lindsten,et al.  Particle gibbs with ancestor sampling , 2014, J. Mach. Learn. Res..

[8]  Sergey Levine,et al.  Variational Policy Search via Trajectory Optimization , 2013, NIPS.

[9]  Fredrik Lindsten,et al.  Backward Simulation Methods for Monte Carlo Statistical Inference , 2013, Found. Trends Mach. Learn..

[10]  Yuval Tassa,et al.  Synthesis and stabilization of complex behaviors through online trajectory optimization , 2012, 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[11]  M. Botvinick,et al.  Planning as inference , 2012, Trends in Cognitive Sciences.

[12]  Alec Solway,et al.  Goal-directed decision making as probabilistic inference: a computational framework and potential neural correlates. , 2012, Psychological review.

[13]  Marc Toussaint,et al.  An Approximate Inference Approach to Temporal Optimization in Optimal Control , 2010, NIPS.

[14]  J. Andrew Bagnell,et al.  Modeling Purposeful Adaptive Behavior with the Principle of Maximum Causal Entropy , 2010 .

[15]  Marc Toussaint,et al.  Robot trajectory optimization using approximate inference , 2009, ICML '09.

[16]  J. Maciejowski,et al.  Sequential Monte Carlo for Model Predictive Control , 2009 .

[17]  Marc Toussaint,et al.  Probabilistic inference for solving discrete and continuous state Markov Decision Processes , 2006, ICML.

[18]  E. Todorov,et al.  A generalized iterative LQG method for locally-optimal feedback control of constrained nonlinear stochastic systems , 2005, Proceedings of the 2005, American Control Conference, 2005..

[19]  Christophe Andrieu,et al.  Particle methods for change detection, system identification, and control , 2004, Proceedings of the IEEE.

[20]  Hagai Attias,et al.  Planning by Probabilistic Inference , 2003, AISTATS.

[21]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[22]  G. Kitagawa The two-filter formula for smoothing and an implementation of the Gaussian-sum smoother , 1994 .

[23]  Leland Stewart,et al.  Use of Bayesian belief networks to fuse continuous and discrete information for target recognition, tracking, and situation assessment , 1992, Defense, Security, and Sensing.

[24]  R. E. Kalman,et al.  When Is a Linear Control System Optimal , 1964 .

[25]  R. E. Kalman,et al.  Contributions to the Theory of Optimal Control , 1960 .