obs[-2 + self.extraRandoms] = 0.1 * sin(angle1) + cartpos
if self.numPoles == 2:
if self.markov:
angle2 = obs[3]
else:
angle2 = obs[1]
obs[-3 + self.extraRandoms] = 0.05 * cos(angle2) + cartpos
obs[-4 + self.extraRandoms] = 0.05 * sin(angle2) + cartpos
if self.extraRandoms > 0:
obs[-self.extraRandoms:] = randn(self.extraRandoms)
if self.verbose:
print('obs', obs)
return obs
def performAction(self, action):
if self.verbose:
print('act', action)
impl.performAction(action[0])
self.addReward()
if __name__ == '__main__':
from pybrain.rl import EpisodicExperiment
from pybrain.rl.agents import FlatNetworkAgent
x = FastCartPoleTask()
a = FlatNetworkAgent(x.outdim, x.indim)
e = EpisodicExperiment(x, a)
e.doEpisodes(2)
# queued version
# experiment._fillQueue(30)
# while True:
# experiment._stepQueueLoop()
# # rewards.append(mean(agent.history.getSumOverSequences('reward')))
# print agent.module.getParameters(),
# print mean(agent.history.getSumOverSequences('reward'))
# clf()
# plot(rewards)
# episodic version
x = 0
batch = 30 #number of samples per gradient estimate (was: 20; more here due to stochastic setting)
while x < 5000:
#while True:
experiment.doEpisodes(batch)
x += batch
reward = mean(
agent.history.getSumOverSequences('reward')) * task.rewardscale
if useGraphics:
pl.addData(0, x, reward)
print agent.module.params
print reward
#if reward > 3:
# pass
agent.learn()
agent.reset()
if useGraphics:
pl.update()
if len(sys.argv) > 2:
if self.numPoles == 2:
if self.markov:
angle2 = obs[3]
else:
angle2 = obs[1]
obs[-3 + self.extraRandoms] = 0.05 * cos(angle2) + cartpos
obs[-4 + self.extraRandoms] = 0.05 * sin(angle2) + cartpos
if self.extraRandoms > 0:
obs[-self.extraRandoms :] = randn(self.extraRandoms)
if self.verbose:
print "obs", obs
return obs
def performAction(self, action):
if self.verbose:
print "act", action
impl.performAction(action[0])
self.addReward()
if __name__ == "__main__":
from pybrain.rl import EpisodicExperiment
from pybrain.rl.agents import FlatNetworkAgent
x = FastCartPoleTask()
a = FlatNetworkAgent(x.outdim, x.indim)
e = EpisodicExperiment(x, a)
e.doEpisodes(2)
# queued version
# experiment._fillQueue(30)
# while True:
# experiment._stepQueueLoop()
# # rewards.append(mean(agent.history.getSumOverSequences('reward')))
# print agent.module.getParameters(),
# print mean(agent.history.getSumOverSequences('reward'))
# clf()
# plot(rewards)
# episodic version
x = 0
batch = 30 #number of samples per gradient estimate (was: 20; more here due to stochastic setting)
while x<5000:
#while True:
experiment.doEpisodes(batch)
x += batch
reward = mean(agent.history.getSumOverSequences('reward'))*task.rewardscale
if useGraphics:
pl.addData(0,x,reward)
print agent.module.params
print reward
#if reward > 3:
# pass
agent.learn()
agent.reset()
if useGraphics:
pl.update()
if len(sys.argv) > 2:
