def run_loaded_agent(self):
input_width = 3
input_height = 4
n_actions = 2
max_iter = 100
discount = 0.9
learn_rate = .005
batch_size = 4
rng = np.random
filename = "agent_max_iter-{}-width-{}-height-{}-discount-{}-lr-{}-batch-{}.npz".format(max_iter,
input_width,
input_height,
discount,
learn_rate,
batch_size)
agent_obj = DeepQLearner(input_width, input_height, n_actions, discount, learn_rate, batch_size, rng)
try:
agent_obj.load(filename)
except:
print "Failed to Load file. Aborting."
return
self.test_agent(agent_obj, input_height, input_width)
D[entry][step] = sequence[entry]
if terminal == 0:
state = state_prime
elif terminal == 1:
state = s1
print('done')
# build the reinforcement-learning agent
print('Building RL agent ... '),
agent = DeepQLearner(
input_width,
input_height,
n_actions,
discount,
learn_rate,
batch_size,
rng
)
print('done')
# begin training
print('Training RL agent ... ')
state = s1 # initialize first state
running_loss = []
for i in range(max_iter):
action = agent.choose_action(state, epsilon) # choose an action using epsilon-greedy policy
state_prime, reward, terminal = world.act(state, action) # get the new state, reward and terminal value from world
def run_episodes(self):
#print('module name:', __name__)
#print('process id:', os.getpid())
# universal learning parameters
input_width = 3
input_height = 4
n_actions = 2
discount = 0.9
learn_rate = .005
batch_size = 4
rng = np.random
replay_size = 16
max_iter = 175
epsilon = 0.2
#TODO: Make this settable from GUI
beginning_state = np.array([[[[0, 0, 0], #pink
[0, 0, 0], #orange
[0, 1, 0], #blue
[0, 0, 0]]]]) #green
print('Starting in 5 seconds... prepare rover opposite to pink flag.')
sleep(5)
# initialize replay memory D <s, a, r, s', t> to replay size with random policy
print('Initializing replay memory ... ')
replay_memory = (
np.zeros((replay_size, 1, input_height, input_width), dtype='int32'),
np.zeros((replay_size, 1), dtype='int32'),
np.zeros((replay_size, 1), dtype=theano.config.floatX),
np.zeros((replay_size, 1, input_height, input_width), dtype=theano.config.floatX),
np.zeros((replay_size, 1), dtype='int32')
)
s1_middle_thirds = beginning_state[0][0][[0, 1, 2, 3], [1, 1, 1, 1]]
terminal = 0
#TODO: STEP 1: Fill with random weights
for step in range(replay_size):
print(step)
mp_lock.acquire()
state = self.last_state.get_last_state()
mp_lock.release()
action = np.random.randint(2)
self.world.act(action)
sleep(0.2)
mp_lock.acquire()
state_prime = self.last_state.get_last_state()
show_cv_frame(self.last_state.get_last_image(), "state_prime")
mp_lock.release()
# get the reward and terminal value of new state
reward, terminal = self.calculate_reward_and_terminal(state_prime)
self.print_color_states(state_prime)
print ('Lead to reward of: {}').format(reward)
sequence = [state, action, reward, state_prime, terminal]
for entry in range(len(replay_memory)):
replay_memory[entry][step] = sequence[entry]
if terminal == 1:
print("Terminal reached, reset rover to opposite red flag. Starting again in 5 seconds...")
print("Resetting back to s1:")
self.reset_rover_to_start(s1_middle_thirds)
print('done')
# build the reinforcement-learning agent
print('Building RL agent ... ')
agent = DeepQLearner(input_width, input_height, n_actions, discount, learn_rate, batch_size, rng)
print('Training RL agent ... Reset rover to opposite pink flag.')
self.reset_rover_to_start(s1_middle_thirds)
print('Starting in 5 seconds...')
sleep(5)
running_loss = []
#TODO: STEP 2: Optimize network
for i in range(max_iter):
mp_lock.acquire()
state = self.last_state.get_last_state()
mp_lock.release()
action = agent.choose_action(state, epsilon) # choose an action using epsilon-greedy policy
# get the new state, reward and terminal value from world
self.world.act(action)
sleep(0.2)
mp_lock.acquire()
state_prime = self.last_state.get_last_state()
show_cv_frame(self.last_state.get_last_image(), "state_prime")
mp_lock.release()
self.print_color_states(state_prime)
reward, terminal = self.calculate_reward_and_terminal(state_prime)
sequence = [state, action, reward, state_prime, terminal] # concatenate into a sequence
print "Found state: "
print state_prime
print ('Lead to reward of: {}').format(reward)
for entry in range(len(replay_memory)):
np.delete(replay_memory[entry], 0, 0) # delete the first entry along the first axis
np.append(replay_memory[entry], sequence[entry]) # append the new sequence at the end
batch_index = np.random.permutation(batch_size) # get random mini-batch indices
loss = agent.train(replay_memory[0][batch_index], replay_memory[1][batch_index],
replay_memory[2][batch_index], replay_memory[3][batch_index],
replay_memory[4][batch_index])
running_loss.append(loss)
#if i % 100 == 0:
print("Loss at iter %i: %f" % (i, loss))
state = state_prime
if terminal == 1:
print("Terminal reached, reset rover to opposite red flag. Starting again in 5 seconds...")
print("Resetting back to s1:")
self.reset_rover_to_start(s1_middle_thirds)
print('... done training')
# test to see if it has learned best route
print("Testing whether optimal path is learned ... set rover to start.\n")
self.reset_rover_to_start(s1_middle_thirds)
filename = "agent_max_iter-{}-width-{}-height-{}-discount-{}-lr-{}-batch-{}.npz".format(max_iter,
input_width,
input_height,
discount,
learn_rate,
batch_size)
agent.save(filename)
#TODO: STEP 3: Test
self.test_agent(agent, input_height, input_width)
sequence = [state, action, reward, state_prime, terminal]
for entry in range(len(D)):
D[entry][step] = sequence[entry]
if terminal == 0:
state = state_prime
elif terminal == 1:
state = s1
print('done')
# build the reinforcement-learning agent
print('Building RL agent ... '),
agent = DeepQLearner(input_width, input_height, n_actions, discount,
learn_rate, batch_size, rng)
print('done')
# begin training
print('Training RL agent ... ')
state = s1 # initialize first state
running_loss = []
for i in range(max_iter):
action = agent.choose_action(
state, epsilon) # choose an action using epsilon-greedy policy
state_prime, reward, terminal = world.act(
state,
action) # get the new state, reward and terminal value from world
sequence = [state, action, reward, state_prime,