SS44: Adaptive Dynamic Programming and Reinforcement Learning


  • Neuroscience and biologically inspired control
  • Finite-sample analysis
  • Applications of ADP and RL
  • RL and ADP-based control
  • Statistical learning
  • Function approximation and value function representation
  • Complexity issues in RL and ADP
  • Policy gradient and actor-critic methods
  • Direct policy search
  • Planning and receding-horizon methods
  • Monte-Carlo tree search and other Monte-Carlo methods
  • Adaptive feature discovery
  • Learning rules and architectures
  • Bayesian RL and exploration
  • Partially observable Markov decision processes
  • ADP and RL for multiplayer games and multiagent systems
  • Distributed intelligent systems
  • Transfer learning
  • Convergence and performance analysis

Paper Submission Format: www.iC3I 2019.org/submissions.html