from PygameEnvironment import TwentyFortyEightEnvironment from pybrain.rl.learners.valuebased import ActionValueNetwork from pybrain.rl.agents import LearningAgent from pybrain.rl.learners import NFQ, SARSA from pybrain.rl.experiments import EpisodicExperiment from pybrain.rl.environments import Task #set the learning time learning_eps = 20 #set the batch size games_per_ep = 25 # make the environment environment = TwentyFortyEightEnvironment() environment.meansize = 1.0/games_per_ep #the task is the game this time task = environment #make the reinforcement learning agent (use a network because inputs are continuous) controller = ActionValueNetwork(task.nsenses,task.nactions) #use Q learning for updating the table (NFQ is for networks) learner = NFQ() agent = LearningAgent(controller, learner)
from pybrain.optimization import GA #HillClimber
from pybrain.rl.agents import OptimizationAgent
from pybrain.rl.experiments import EpisodicExperiment
from pybrain.rl.environments import Task
#set the learning time
learning_eps = 150
#set the batch size
games_per_ep = 75
population_size = 150
# make the environment
environment = TwentyFortyEightEnvironment()
environment.meansize = 1.0/(population_size*games_per_ep)
#the task is the game this time
task = environment
#create our network
controller = buildNetwork(task.nsenses, 20, task.nactions)
#use a Genetic Algorithm
#all the commented out lines are options you can play with
learner = GA(populationSize=population_size
, topProportion=0.05
, elitism=True
, eliteProportion=0.5
, mutationProb=0.05
