Java Deep Learning Cookbook

How to do it...

1. Create an action space for the mission:

Sample: MalmoActionSpaceDiscrete actionSpace = new MalmoActionSpaceDiscrete("movenorth 1", "movesouth 1", "movewest 1", "moveeast 1"); actionSpace.setRandomSeed(rndSeed); 

2. Create an observation space for the mission:

MalmoObservationSpace observationSpace = new MalmoObservationSpacePixels(xSize, ySize);

3. Create a Malmo consistency policy:

MalmoDescretePositionPolicy obsPolicy = new MalmoDescretePositionPolicy();

4. Create an MDP (short for Markov Decision Process) wrapper around the Malmo Java client:

Sample: MalmoEnv mdp = new MalmoEnv("cliff_walking_rl4j.xml", actionSpace, observationSpace, obsPolicy);

5. Create a DQN using DQNFactoryStdConv:

Sample: public static DQNFactoryStdConv.Configuration ...