Covers algorithms for optimal sequential decision making in the presence of uncertainty. Mathematical formalisms include the Markov decision process (MDP), partially observable Markov decision process (POMDP), and Games. Solution techniques include exact dynamic programming, Monte Carlo tree search, deep reinforcement learning, and alpha vector value approximation for POMDPs.
This project has been created by Giovanni Fereoli in 2024. For any problem, clarification or suggestion, you can contact the author at [email protected].
The package is under the MIT license.