# 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")