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)
# set parameters from command line
# create agent
agent = LearningAgent(net, None)
agent.module._setParameters(
array([
float(sys.argv[1]),
float(sys.argv[2]),
float(sys.argv[3]),
float(sys.argv[4])
]))
agent.disableLearning()
# create experiment
experiment = EpisodicExperiment(task, agent)
experiment.doEpisodes(episodes)
ret = []
for n in range(agent.history.getNumSequences()):
returns = agent.history.getSequence(n)
reward = returns[2]
ret.append(sum(reward, 0).item())
print ret, "mean:", mean(ret)
env.getRenderer().stop()
if len(sys.argv) < 5:
sys.exit('please give 4 parameters. run: "python play.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)
# set parameters from command line
# create agent
agent = LearningAgent(net, None)
agent.module._setParameters(array([float(sys.argv[1]), float(sys.argv[2]), float(sys.argv[3]), float(sys.argv[4])]))
agent.disableLearning()
# create experiment
experiment = EpisodicExperiment(task, agent)
experiment.doEpisodes(episodes)
ret = []
for n in range(agent.history.getNumSequences()):
returns = agent.history.getSequence(n)
reward = returns[2]
ret.append( sum(reward, 0).item() )
print ret, "mean:",mean(ret)
env.getRenderer().stop()
