experiment.addAdapter(discretizer)
# add e-greedy exploration
explorer = EpsilonGreedyExplorer(0.3, episodeCount=1000)
experiment.addAdapter(explorer)
# force experiment setup now
experiment.setup()
for i in range(len(discretizer.stateVectors)):
plt.text(discretizer.stateVectors[i,0], discretizer.stateVectors[i,1], "%i"%i, bbox=dict(facecolor='green', alpha=0.5))
plt.xlim(-2.5, 2.5)
plt.ylim(-10, 10)
plt.show()
agent.forget()
explorer.epsilon = 0.3
# renderer = CartPoleRenderer()
# environment.renderer = renderer
# renderer.start()
# run experiment
for i in range(1000):
experiment.runEpisode(reset=True)
discretizer.adaptClusters()
agent.learn()
print "sum rewards:", sum(agent.episode.rewards)
print "exploration:", explorer.epsilon
from numpy import *
from dopamine.adapters import VQStateDiscretizationAdapter
from matplotlib import pyplot as plt
plt.ion()
vq = VQStateDiscretizationAdapter(50)
states = random.normal([0, 3], [3, 1], (500, 2))
states = r_[states, random.normal([-1, -2], [0.5, 2], (200, 2))]
plt.plot(states[:,0], states[:,1], '.')
for s in states:
vq.applyState(s)
vq.sampleClusters()
vq.adaptClusters()
for i in range(len(vq.stateVectors)):
plt.text(vq.stateVectors[i,0], vq.stateVectors[i,1], "%i"%i, bbox=dict(facecolor='green', alpha=0.5))
plt.ylim(-10, 10)
plt.xlim(-10, 10)
plt.show()
