Directory Structure
------
.
└── Search-based Planning
└── Search_2D
├── bfs.py # breadth-first searching
├── dfs.py # depth-first searching
├── dijkstra.py # dijkstra's
├── a_star.py # A*
├── bidirectional_a_star.py # Bidirectional A*
├── ARAstar.py # Anytime Reparing A*
├── IDAstar.py # Iteratively Deepening A*
├── LRTAstar.py # Learning Real-time A*
├── RTAAstar.py # Real-time Adaptive A*
├── LPAstar.py # Lifelong Planning A*
├── D_star.py # D* (Dynamic A*)
└── D_star_Lite.py # D* Lite
└── Search_3D
├── Astar3D.py # A*_3D
├── bidirectional_Astar3D.py # Bidirectional A*_3D
├── RTA_Astar3D.py # Real-time Adaptive A*_3D
└── LRT_Astar3D.py # Learning Real-time A*_3D
└── gif # Animations
└── Sampling-based Planning
└── rrt_2D
├── rrt.py # rrt : goal-biased rrt
└── rrt_star.py
└── rrt_3D
├── rrt3D.py # rrt3D : goal-biased rrt3D
└── rrtstar3D.py
└── Stochastic Shortest Path
├── value_iteration.py # value iteration
├── policy_iteration.py # policy iteration
├── Q-value_iteration.py # Q-value iteration
└── Q-policy_iteration.py # Q-policy iteration
└── Model-free Control
├── Sarsa.py # SARSA : on-policy TD control
└── Q-learning.py # Q-learning : off-policy TD control
## Animations
### DFS & BFS (Dijkstra)
* Blue: starting state
* Green: goal state
### A* and A* Variants
### Value/Policy/Q-value/Q-policy Iteration
* Brown: losing states
### SARSA(on-policy) & Q-learning(off-policy)
* Brown: losing states
## License
MIT License
