OptimizedDP: An Efficient, User-friendly Library For Optimal Control and Dynamic Programming

Understand

This paper introduces OptimizedDP, a high-performance software library for several common grid-based dynamic programming (DP) algorithms used in control theory and robotics.

• Specifically, OptimizedDP provides functions to numerically solve a class of time-dependent (dynamic) Hamilton-Jacobi (HJ) partial differential equations (PDEs), time-independent (static) HJ PDEs, and additionally value iteration for continuous action-state space Markov Decision Processes (MDP).
• The computational complexity of grid-based DP is exponential with respect to the number of grid or state space dimensions, and thus can have bad execution runtimes and memory usage whenapplied to large state spaces.
• We leverage the user-friendliness of Python for different problem specifications without sacrificing the efficiency of the core computation.