def __init__(self,
skip_frame,
num_actions,
load=None,
random_state=np.random.RandomState()):
super().__init__()
rand_vals = (
1, 0.1, 1000000
) # starting at 1 anneal eGreedy policy to 0.1 over 1,000,000 actions
self.action_handler = ActionHandler(rand_vals)
self.minimum_replay_size = 100
self.exp_handler = DataSet(84,
84,
random_state,
max_steps=1000000,
phi_length=skip_frame)
self.cnn = CNN((None, skip_frame, 84, 84), num_actions)
self.skip_frame = skip_frame
self.discount = .95
self.costList = list()
self.state_tm1 = None
if load is not None:
self.cnn.load(load)
def __init__(self, skip_frame, num_actions, load, rand_val=0.05):
rand_vals = (rand_val, rand_val, 2)
self.action_handler = ActionHandler(rand_vals)
self.cnn = CNN((None, skip_frame, 84, 84), num_actions, 1)
self.cnn.load(load)
self.q_vals = list()
self.skip_frame = skip_frame
self.exp_handler = DataSet(84,
84,
np.random.RandomState(),
phi_length=skip_frame)
self.skip_frame = skip_frame
self.state_tm1 = np.zeros((84, 84), dtype=np.uint8)
def __init__(self, skip_frame, num_actions):
rand_vals = (
1, 0.1, 1000000
) # starting at 1 anneal eGreedy policy to 0.1 over 1,000,000 actions
self.action_handler = ActionHandler(ActionPolicy.eGreedy, rand_vals)
self.minimum_replay_size = 100
self.exp_handler = DataSet(84,
84,
np.random.RandomState(),
max_steps=1000000,
phi_length=skip_frame)
self.cnn = CNN((None, skip_frame, 84, 84), num_actions)
self.skip_frame = skip_frame
self.costList = list()
self.state_tm1 = None
# novelty setup
self.frame_table = dict()
self.new_novel_states = 0
def __init__(self, num_actions, initial_cnn_values, cnn_partial, pipe,
skip_frame=4, phi_length=4, async_update_step=5, target_update_frames=40000):
super().__init__(pipe)
# initialize action handler, ending E-greedy is either 0.1, 0.01, 0.5 with probability 0.4, 0.3, 0.3
end_rand = np.random.choice([0.1, 0.01, 0.5], p=[0.4, 0.3, 0.3])
rand_vals = (1, end_rand, 4000000) # anneal over four million frames
self.action_handler = ActionHandler(rand_vals)
# initialize cnn
self.cnn = cnn_partial()
self.cnn.set_parameters(initial_cnn_values)
self.cnn.set_target_parameters(initial_cnn_values)
self.frame_buffer = np.zeros((1, phi_length, 84, 84), dtype=np.float32)
self.skip_frame = skip_frame
self.phi_length = phi_length
self.loss_list = list()
self.async_update_step = async_update_step
self.target_update_frames = target_update_frames
self.target_update_count = 0
def __init__(self,
num_actions,
initial_cnn_values,
cnn_partial,
pipe,
skip_frame=4,
phi_length=4,
async_update_step=5):
super().__init__(pipe)
# A3C doesn't have an EGreedy exploration policy so we set the random values to 0
self.action_handler = ActionHandler((0, 0, 2))
# initialize cnn
self.cnn = cnn_partial()
self.cnn.set_parameters(initial_cnn_values)
self.frame_buffer = np.zeros((1, phi_length, 84, 84), dtype=np.float32)
self.skip_frame = skip_frame
self.phi_length = phi_length
self.loss_list = list()
self.async_update_step = async_update_step
def __init__(self, output_fn):
rand_vals = (0.05, 0.05, 2)
self.action_handler = ActionHandler(ActionPolicy.eGreedy, rand_vals)
self.last_img = None
self.output_fn = output_fn
def action_handler():
act = ActionHandler(ActionPolicy.eGreedy, [1, 0.1, 2])
return act
def test_rand_vals():
# just test to make sure rand vals doesn't fail
action_handler = ActionHandler(ActionPolicy.randVals, [1, 0.1, 2],
[0, 2, 4, 6])
action_handler.get_action([0, 0, 0, 0])
def main():
import os
import pickle
import matplotlib.pyplot as plt
from learningALE.learners.nns import CNN
from scipy.misc import imresize
from learningALE.handlers.actionhandler import ActionHandler, ActionPolicy
from learningALE.libs.ale_python_interface import ALEInterface
import lasagne
import numpy as np
# plt.ion()
skipFrame = 3
cnn = CNN((None, skipFrame, 86, 80), 6, .1, stride=(4, 2))
with open(os.getcwd() + '\datasets\\spccnn.pkl', 'rb') as infile:
parms = pickle.load(infile)
lasagne.layers.set_all_param_values(cnn.l_out, parms)
# rom = b'D:\\_code\\breakout.bin'
rom = b'D:\\_code\\space_invaders.bin'
ale = ALEInterface(True)
ale.loadROM(rom)
(screen_width, screen_height) = ale.getScreenDims()
legal_actions = ale.getMinimalActionSet()
# get labels
labels = [
'noop', 'fire', 'up', 'right', 'left', 'down', 'upright', 'upleft',
'downright', 'downleft', 'upfire', 'rightfire', 'leftfire', 'downfire',
'uprightfire', 'upleftfire', 'downrightfire', 'downleftfire'
]
labels = np.asarray(labels)[legal_actions]
# set up vars
actionHandler = ActionHandler(ActionPolicy.eGreedy, (.1, .1, 2),
legal_actions)
rewList = list()
for ep in range(100):
total_reward = 0.0
trainCount = 0
ale.reset_game()
while not ale.game_over():
# get frames
frames = list()
reward = 0
for frame in range(skipFrame):
gamescreen = ale.getScreenRGB()
processedImg = np.asarray(gamescreen.view(np.uint8).reshape(
screen_height, screen_width, 4)[25:-12, :, 0],
dtype=np.float32)
processedImg[processedImg > 1] = 255
processedImg = imresize(processedImg, 0.5,
interp='nearest') / 255
frames.append(processedImg)
performedAction, actionInd = actionHandler.getLastAction()
rew = ale.act(performedAction)
if rew > 0:
rew = 1
reward += rew
total_reward += reward
frames = np.asarray(frames, dtype=np.float32)
actionVect = cnn.get_output(
frames.reshape((1, skipFrame, frames.shape[1], 80)))[0]
actionHandler.setAction(actionVect)
# hid1_act = cnn.get_hid1_act(frames.reshape((1, skip_frame, frames.shape[1], 80)))
# hid2_act = cnn.get_hid2_act(frames.reshape((1, skip_frame, frames.shape[1], 80)))
# for x in range(hid1_act.shape[1]):
# plt.subplot(4,4,x+1)
# plt.imshow(hid1_act[0,x], cmap=plt.cm.gray)
# for x in range(hid2_act.shape[1]):
# plt.subplot(6,6,x+1)
# plt.imshow(hid2_act[0,x], cmap=plt.cm.gray)
# plt.show()
# plt.clf()
# plt.plot(actionVect)
# plt.xticks(range(len(labels)), labels)
# plt.pause(0.001)
rewList.append(total_reward)
print(ep, total_reward)
print(np.mean(rewList), np.std(rewList), np.max(rewList), np.min(rewList))
print(np.unique(rewList, return_counts=True))
plt.plot(rewList)
plt.show()
