BocopHJB 1.0.1 is out!
The BocopHJB package implements a global optimization method. Similarly to the Dynamic Programming approach, the optimal control problem is solved in two steps. First we solve the Hamilton-Jacobi-Bellman equation satisfied by the value function of the problem. Then we simulate the optimal trajectory from any chosen initial condition.
Key features:
- Global optimization for both deterministic and stochastic optimal control problems.
- Handles switching between discrete modes of the system.
- Stopping time problems can be solved using switching.
- Built-in simulation module to recompute optimal strategies.
- Support advanced rules to define the discrete control set.
- Parallel execution with OpenMP.
- Matlab/Python scripts to read the value function and simulated trajectories.
