def test():
env = gym.make('HumanoidDeepMimicWalkBulletEnv-v1')
env.render()
actor_learning_rate = 0.00001
critic_learning_rate = 0.0001
gamma = .99 # .95
tau = .001 # .125
actor = Actor(env, actor_learning_rate, tau)
critic = Critic(env, critic_learning_rate, gamma, tau)
actor.model.load_weights(
checkpoint_path.format(training_num=restore_training_num,
model='actor_model',
epoch=restore_epoch))
actor.target_model.load_weights(
checkpoint_path.format(training_num=restore_training_num,
model='actor_target',
epoch=restore_epoch))
critic.model.load_weights(
checkpoint_path.format(training_num=restore_training_num,
model='critic_model',
epoch=restore_epoch))
critic.target_model.load_weights(
checkpoint_path.format(training_num=restore_training_num,
model='critic_target',
epoch=restore_epoch))
cur_state = env.reset()
for _ in range(100000):
action = actor.act(
cur_state.reshape((1, env.observation_space.shape[0])))
new_state, reward, done, _ = env.step(
action.reshape((1, env.action_space.shape[0]))[0])
cur_state = new_state
time.sleep(.1)
if done:
break
import time
from composer import Composer
from actor import Actor
if __name__ == '__main__':
# File where actions will be stored
actions = "actions.act"
# Composer that will record inputs for 5 seconds
cp = Composer(actions, time=5)
print("**Composing**")
# Start composing
cp.record()
while cp.is_running():
cp.update()
time.sleep(0.1)
# Do other stuff here...
# Actor that will play the act
ac = Actor(actions)
print("\n**Acting**")
# Start acting
ac.act()
def main():
env = gym.make('HumanoidDeepMimicWalkBulletEnv-v1')
actor_learning_rate = 0.00001
critic_learning_rate = 0.0001
gamma = .99 #.95
tau = .001 #.125
actor = Actor(env, actor_learning_rate, tau)
critic = Critic(env, critic_learning_rate, gamma, tau)
buffer = Buffer()
if restore_epoch >= 0:
actor.model.load_weights(
checkpoint_path.format(training_num=restore_training_num,
model='actor_model',
epoch=restore_epoch))
actor.target_model.load_weights(
checkpoint_path.format(training_num=restore_training_num,
model='actor_target',
epoch=restore_epoch))
critic.model.load_weights(
checkpoint_path.format(training_num=restore_training_num,
model='critic_model',
epoch=restore_epoch))
critic.target_model.load_weights(
checkpoint_path.format(training_num=restore_training_num,
model='critic_target',
epoch=restore_epoch))
num_epochs = 50000
epoch_len = 1000
if render:
env.render()
for epoch in range(num_epochs):
print("Epoch {}".format(epoch))
cur_state = env.reset()
max_train_reward = 0
max_reward = 0
for j in range(epoch_len):
action = actor.act(
cur_state.reshape((1, env.observation_space.shape[0])))
new_state, reward, done, _ = env.step(
action.reshape((1, env.action_space.shape[0]))[0])
buffer.remember(cur_state, action, reward, new_state, done)
max_reward = max(max_reward, reward)
if len(buffer.memory) > batch_size:
avg_reward = run_training(actor, critic, buffer)
max_train_reward = max(max_train_reward, avg_reward)
cur_state = new_state
actor.update_target_network()
critic.update_target_network()
if done:
break
print('Max_reward {}\nAvg_max_train_reward {}\n'.format(
max_reward, max_train_reward / epoch_len))
if make_checkpoints and (epoch % checkpoints_freq == 0):
actor.model.save_weights(
checkpoint_path.format(training_num=training_num,
model='actor_model',
epoch=epoch))
actor.target_model.save_weights(
checkpoint_path.format(training_num=training_num,
model='actor_target',
epoch=epoch))
critic.model.save_weights(
checkpoint_path.format(training_num=training_num,
model='critic_model',
epoch=epoch))
critic.target_model.save_weights(
checkpoint_path.format(training_num=training_num,
model='critic_target',
epoch=epoch))
if make_checkpoints and (epoch % hyperparam_freq == 0):
file_path = hyperparam_path.format(training_num=training_num)
with open(file_path, 'a') as f:
f.write('{},{}\n'.format(max_reward,
max_train_reward / epoch_len))
# Diagonal wall """d = 5 for i in xrange(d, size): environment.obstacles[(i, i - d)] = '#' del environment.obstacles[(7, 2)]""" # Specific points """environment.obstacles[(9, 4)] = '#' environment.obstacles[(8, 6)] = '#'""" # DEBUG #print "\n"*100 actor.move(end) actor.act() """for i in actor.actions: print i.position.as_tuple()""" environment.print_state() """while actor.can_act(): sleep(0.10) a = actor.act() actor.state = a print '\n'*50 print a.as_tuple() environment.print_state() if not actor.can_act(): break"""
