# make a PLE instance.
p = PLE(game,
fps=fps,
frame_skip=frame_skip,
num_steps=num_steps,
force_fps=force_fps,
display_screen=display_screen)
# our Naive agent!
agent = NaiveAgent(p.getActionSet())
# init agent and game.
p.init()
# lets do a random number of NOOP's
for i in range(np.random.randint(0, max_noops)):
reward = p.act(p.NOOP)
# start our training loop
for f in range(nb_frames):
# if the game is over
if p.game_over():
p.reset_game()
obs = p.getScreenRGB()
action = agent.pickAction(reward, obs)
reward = p.act(action)
if f % 50 == 0:
p.saveScreen("screen_capture.png")
print(IPost)
print('actionIndex')
print(actionIndex)
print('reward: %f'%(reward))
print('rewardAfterAdapt: %f'%(rewardAfterAdapt))
print('points: %f'%(points))
if recording and step%recordingSample==0:
Trace['actionIndex'][step/recordingSample]=actionIndex
Trace['reward'][step/recordingSample]=reward
Trace['points'][step/recordingSample]=points
Trace['rewardIncrease'][step/recordingSample]=rewardIncrease
Trace['rewardAdaption'][step/recordingSample]=rewardAdaption
ADSA.Recording()
ALIFNArray.Record()
if saveVideo and step>StarRcordFrames:
p.saveScreen(capturePath+'frame%.9d.png'%(p.getFrameNumber()))
#%%
if not os.path.exists(path):
os.makedirs(path)
codepath=path+'src/'
if not os.path.exists(codepath):
os.makedirs(codepath)
for filename in os.listdir(os.getcwd()):
if filename.endswith(".py"):
shutil.copy2(filename, codepath)
NeuonNumber=0
newSlice= [slice(None)]*3
newSlice[1]=NeuonNumber
Traces = Tracet, ADSA.Trace['Weighters'][newSlice], ADSA.Trace['WeighterVarRates'][newSlice], ADSA.Trace['WeighterInAxonConcentration'][newSlice], ADSA.Trace['WeightersCentre'][newSlice], ADSA.Trace['WeighterVarDamping'][newSlice], ADSA.Trace['EquivalentVolume'][newSlice]
# figure1,figure2,figure3,figure4,figure5, figure6, figure7,figure8,figure9,ax = DSA.plot(TimOfRecording, Traces, path=path, savePlots=savePlots, StartTimeRate=1, linewidth= linewidth) #path=
reward = 0.0
max_noops = 20
nb_frames = 15000
#make a PLE instance.
p = PLE(game, fps=fps, frame_skip=frame_skip, num_steps=num_steps,
force_fps=force_fps, display_screen=display_screen)
#our Naive agent!
agent = NaiveAgent(p.getActionSet())
#init agent and game.
p.init()
#lets do a random number of NOOP's
for i in range(np.random.randint(0, max_noops)):
reward = p.act(p.NOOP)
#start our training loop
for f in range(nb_frames):
#if the game is over
if p.game_over():
p.reset_game()
obs = p.getScreenRGB()
action = agent.pickAction(reward, obs)
reward = p.act(action)
if f % 50 == 0:
p.saveScreen("screen_capture.png")
