from pybrain.rl.environments.cartpole import CartPoleEnvironment, CartPoleRenderer, BalanceTask from pybrain.rl.agents.learning import LearningAgent from pybrain.rl.experiments import EpisodicExperiment from scipy import mean import sys episodes = 1 epilen = 200 if len(sys.argv) < 5: sys.exit('please give 4 parameters. run: "python play_catpole.py <p1> <p2> <p3> <p4>"\n') # create environment env = CartPoleEnvironment() env.setRenderer(CartPoleRenderer()) env.getRenderer().start() env.delay = (episodes == 1) # create task task = BalanceTask(env, epilen) # create controller network net = buildNetwork(4, 1, bias=False) # create agent and set parameters from command line agent = LearningAgent(net, None) agent.module._setParameters([float(sys.argv[1]), float(sys.argv[2]), float(sys.argv[3]), float(sys.argv[4])]) # create experiment experiment = EpisodicExperiment(task, agent) experiment.doEpisodes(episodes)
from pybrain.rl.agents.learning import LearningAgent from pybrain.rl.experiments import EpisodicExperiment from scipy import mean import sys import pylab episodes = 1 epilen = 200 if len(sys.argv) < 5: sys.exit('please give 4 parameters. run: "python play_catpole.py <p1> <p2> <p3> <p4>"\n') # create environment env = CartPoleEnvironment() env.setRenderer(CartPoleRenderer()) env.getRenderer().start() env.delay = (episodes == 1) # create task task = BalanceTask(env, epilen) # create controller network net = buildNetwork(4, 1, bias=False) # create agent and set parameters from command line agent = LearningAgent(net, None) arg1 = float(sys.argv[1]) arg2 = float(sys.argv[2]) arg3 = float(sys.argv[3]) arg4 = float(sys.argv[4]) agent.module._setParameters([arg1, arg2, arg3, arg4])