This series, we deal with zero-sum turn-based board game algorithm, a sub type of combinatorial games. We start off with small search space problem, introduce classic algorithms and corresponding combinatorial gaming theory and ultimately end with modern approximating Deep RL techniques. From there, after stepping stone is laid, we are able to learn and appreciate how AlphaGo works. In this first episode, we illustrate 3 classic gaming problems in leetcode and solve them from brute force version to DP version then finally rewrite them using classic gaming algorithms, minimax and alpha beta pruning.
This is second episode of series: TSP From DP to Deep Learning.
Episode 1: AC TSP on AIZU with recursive DP Episode 2: TSP DP on a Euclidean Dataset Episode 3: Pointer Networks in PyTorch Episode 4: Search for Most Likely Sequence Episode 5: Reinforcement Learning PyTorch Implementation AIZU TSP Bottom Up Iterative DP In last episode, we provided a top down recursive DP in Python 3 and Java 8.
Travelling salesman problem (TSP) is a classic NP hard computer algorithmic problem. In this series, we will first solve TSP problem in an exact manner by ACing TSP on aizu with dynamic programming, and then move on to train a Pointer Network with Pytorch to obtain an approximate solution with deep learning and reinforcement learning technology. Complete episodes are listed as follows:
Episode 1: AC TSP on AIZU with recursive DP