def symmetryData(data):
symmetryData = History(data.stateDim, data.actionDim)
for e in data:
symmetryData.appendEpisode(e)
# for e in data:
# for s, a, r, _ in e:
# symmetryData.append(-s, -a, r)
# symmetryData.newEpisode()
return symmetryData
def evaluateEpisodes(self, count, reset=True, exploration=False, visualize=True):
self.setup()
# tell agent that evaluation is taking place
self.agent.evaluation = True
currNumEpisodes = len(self.agent.history)
# disable all explorers and store them for later
if not exploration:
explorers = []
for a in self.adapters_:
if isinstance(a, Explorer) and a.active:
explorers.append(a)
a.active = False
# run experiment for evaluation and store history
self.runEpisodes(count, reset)
self.numEpisodes -= count
# copy the latest episodes to a new history
history = History(self.agent.history.stateDim, self.agent.history.actionDim)
history.episodes_ = self.agent.history.episodes_[currNumEpisodes:-1]
# remove the evaluation histories from the agent
self.agent.history.episodes_ = self.agent.history.episodes_[:currNumEpisodes] + [self.agent.history.episodes_[-1]]
# enable exploration again if disabled before
if not exploration:
for a in explorers:
a.active = True
# tell agent that evaluation is over
self.agent.evaluation = False
if visualize:
plt.ion()
plt.clf()
self.visual_x.append(self.numEpisodes)
self.visual_y.append(mean([sum(e.rewards) for e in history]))
plt.plot(self.visual_x, self.visual_y, 'o-', color='black')
plt.gcf().canvas.draw()
return history
def reduceData(data, buffer=4):
""" goes through dataset and only stores the "interesting"
samples, the ones where a change in reward has happened.
"""
reducedHistory = History(data.stateDim, data.actionDim)
transitions = []
for episode in data:
rdiff = episode.rewards[1:] - episode.rewards[:-1]
transitions = set([0, len(episode.rewards)-1])
transitions.update(where(rdiff != 0)[0])
buf = set()
for t in transitions:
for i in range(max(0, t-buffer), min(t+2+buffer, len(episode.rewards))):
buf.add(i)
for b in buf:
reducedHistory.append(episode.states[b,:], episode.actions[b,:], episode.rewards[b])
reducedHistory.newEpisode()
return reducedHistory
