def main():
env = gym.make('LunarLanderContinuous-v2')
log_dir = 'log/lander'
# env = gym.make('Pendulum-v0')
# log_dir = 'log/pendulum'
# paper settings
# agent = DDPG(env, sigma=0.2, num_episodes=1000, buffer_size=1000000, batch_size=64,
# tau=1e-3, batch_norm=True, merge_layer=2)
# did not work unless I merged action into critic at first layer
# worked btter without batchnorm
k = 4000
"""
agent = DDPG(env, log_dir, sigma=0.2, num_episodes=k, buffer_size=1000000, batch_size=64,
tau=1e-3, batch_norm=False, merge_layer=0)
print('training start')
agent.train()
"""
agent = DDPG(env,
log_dir,
sigma=0.2,
num_episodes=k,
buffer_size=1000000,
batch_size=64,
tau=1e-2,
batch_norm=False,
merge_layer=0)
print('training1 start')
agent.train1()
"""
def main(args):
env = gym.make(args['env_name'])
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
action_dim = env.action_space.shape[0]
max_action = env.action_space.high[0]
state_dim = env.observation_space.shape[0]
ddpg = DDPG(args, action_dim, max_action, state_dim, device)
trained_actor = torch.load(args['model_directory'])
ddpg.actor.load_state_dict(trained_actor)
timestep = 0
for episode in range(args['max_episode']):
episode_reward = 0
state = env.reset()
while True:
action = ddpg.get_action(state)
next_state, reward, done, info = env.step(action)
env.render()
episode_reward += reward
state = next_state
timestep += 1
if done:
print('episode: ', episode,
' reward : %.3f' % (episode_reward), ' timestep :',
timestep)
break
def Test():
print 'test'
cmd = 'sudo python ./utils/delete_episode_data.py'
os.system(cmd)
env = NetworkEnv()
agent = DDPG(env.state_dim, env.rate_dim, env.path_dim, env.action_dim,
env.kPath)
rewards = []
workload = datamining
state = env.reset(workload)
agent.load()
t1 = time.time()
for i in range(test_max_episode):
Perform = []
ep_r = 0.0
if i % 5 == 0:
if env.CDF_file == datamining:
env.set_CDF_file(websearch)
else:
env.set_CDF_file(datamining)
for t in range(test_max_step):
print '\n'
print '第%d回合,第%d步骤:' % (i, t)
print 'state', state
action = agent.select_action(state)
print 'action:', action
# execute action
next_state, reward, perform = env.step(action)
Perform.append(perform)
ep_r += reward
state = next_state
print 'reward', reward
print(
"Ep_i {}, the ep_r is {:0.2f}, the step is {}, the reward is {}"
.format(i, ep_r, t, reward))
if t == test_max_step - 1:
print("Ep_i {}, the ep_r is {:0.2f}".format(i, ep_r))
df_perform = pd.DataFrame(Perform)
df_perform.to_csv("./data/test_perform.csv",
mode='a',
header=False,
index=False)
break
rewards.append(ep_r)
file_time = open('./data/test_time.txt', mode='w')
df = pd.DataFrame([rewards])
df.to_csv("./data/test_rewards.csv", mode='w', header=False, index=False)
print max_episode, 'episode的总运行时间:', time.time() - t1
file_time.write(str(time.time() - t1))
def main():
ddpg = DDPG(0, 0, torch.cuda.is_available())
env.init_state()
if os.path.exists('models/ddpg_actor_'):
ddpg.load_model()
else:
print("Please ensure models existing!")
while True:
action = ddpg.select_action(env.state)
env.step(action)
print(env.last_score)
def main():
# Initialize the ANNs
agent = DDPG()
rospy.init_node("neuro_deep_planner", anonymous=False)
ros_handler = ROSHandler()
ros_handler.on_policy = False
while not rospy.is_shutdown():
# If we have a new msg we might have to execute an action and need to put the new experience in the buffer
if ros_handler.new_msg():
if not ros_handler.is_episode_finished:
# Send back the action to execute
ros_handler.publish_action(agent.get_action(ros_handler.state))
# Safe the past state and action + the reward and new state into the replay buffer
agent.set_experience(ros_handler.state, ros_handler.reward, ros_handler.is_episode_finished)
elif ros_handler.new_setting():
agent.noise_flag = ros_handler.noise_flag
else:
# Train the network!
agent.train()
def main():
experiment = 'model-builder-v0' #specify environments here
env = gym.make(experiment)
#steps= env.spec.timestep_limit #steps per episode
steps = 20
assert isinstance(env.observation_space,
Box), "observation space must be continuous"
assert isinstance(env.action_space, Box), "action space must be continuous"
#Randomly initialize critic,actor,target critic, target actor network and replay buffer
agent = DDPG(env, is_batch_norm)
exploration_noise = OUNoise(env.action_space.shape[0])
counter = 0
reward_per_episode = 0
total_reward = 0
num_states = env.observation_space.shape[0]
num_actions = env.action_space.shape[0]
print("Number of States:", num_states)
print("Number of Actions:", num_actions)
print("Number of Steps per episode:", steps)
#saving reward:
reward_st = np.array([0])
for i in range(episodes):
print("==== Starting episode no:", i, "====", "\n")
observation = env.reset()
reward_per_episode = 0
for t in range(steps):
#rendering environmet (optional)
env.render()
x = observation
action = agent.evaluate_actor(np.reshape(x, [1, 300, 300, 2]))
noise = exploration_noise.noise()
action = action[
0] + noise #Select action according to current policy and exploration noise
print("Action at step", t, " :", action, "\n")
observation, reward, done, info = env.step(action)
#add s_t,s_t+1,action,reward to experience memory
agent.add_experience(x, observation, action, reward, done)
#train critic and actor network
if counter > 64:
agent.train()
reward_per_episode += reward
counter += 1
#check if episode ends:
if (done or (t == steps - 1)):
print('EPISODE: ', i, ' Steps: ', t, ' Total Reward: ',
reward_per_episode)
print("Printing reward to file")
exploration_noise.reset(
) #reinitializing random noise for action exploration
reward_st = np.append(reward_st, reward_per_episode)
np.savetxt('episode_reward.txt', reward_st, newline="\n")
print('\n\n')
break
total_reward += reward_per_episode
print("Average reward per episode {}".format(total_reward / episodes))
def main():
experiment= 'InvertedPendulum-v1' #specify environments here
env= gym.make(experiment)
steps= env.spec.timestep_limit #steps per episode
assert isinstance(env.observation_space, Box), "observation space must be continuous"
assert isinstance(env.action_space, Box), "action space must be continuous"
#Randomly initialize critic,actor,target critic, target actor network and replay buffer
agent = DDPG(env, is_batch_norm)
exploration_noise = OUNoise(env.action_space.shape[0])
counter=0
reward_per_episode = 0
total_reward=0
num_states = env.observation_space.shape[0]
num_actions = env.action_space.shape[0]
print "Number of States:", num_states
print "Number of Actions:", num_actions
print "Number of Steps per episode:", steps
#saving reward:
reward_st = np.array([0])
for i in xrange(episodes):
print "==== Starting episode no:",i,"====","\n"
observation = env.reset()
reward_per_episode = 0
for t in xrange(steps):
#rendering environmet (optional)
env.render()
x = observation
action = agent.evaluate_actor(np.reshape(x,[1,num_states]))
noise = exploration_noise.noise()
action = action[0] + noise #Select action according to current policy and exploration noise
print "Action at step", t ," :",action,"\n"
observation,reward,done,info=env.step(action)
#add s_t,s_t+1,action,reward to experience memory
agent.add_experience(x,observation,action,reward,done)
#train critic and actor network
if counter > 64:
agent.train()
reward_per_episode+=reward
counter+=1
#check if episode ends:
if (done or (t == steps-1)):
print 'EPISODE: ',i,' Steps: ',t,' Total Reward: ',reward_per_episode
print "Printing reward to file"
exploration_noise.reset() #reinitializing random noise for action exploration
reward_st = np.append(reward_st,reward_per_episode)
np.savetxt('episode_reward.txt',reward_st, newline="\n")
print '\n\n'
break
total_reward+=reward_per_episode
print "Average reward per episode {}".format(total_reward / episodes)
from strategies import OUStrategy
from utils import SEED
import mxnet as mx
# set environment, policy, qfunc, strategy
env = normalize(CartpoleEnv())
policy = DeterministicMLPPolicy(env.spec)
qfunc = ContinuousMLPQ(env.spec)
strategy = OUStrategy(env.spec)
# set the training algorithm and train
algo = DDPG(
env=env,
policy=policy,
qfunc=qfunc,
strategy=strategy,
ctx=mx.gpu(0),
max_path_length=100,
epoch_length=1000,
memory_start_size=10000,
n_epochs=1000,
discount=0.99,
qfunc_lr=1e-3,
policy_lr=1e-4,
seed=SEED)
algo.train()
def train(env, nb_epochs, nb_epoch_cycles, normalize_observations, actor_lr, critic_lr, action_noise,
gamma, nb_train_steps, nb_rollout_steps, batch_size, memory, tau=0.01):
max_action = env.action_space.high
agent = DDPG(memory, env.observation_space.shape[0], env.action_space.shape[0],
gamma=gamma, tau=tau,
normalize_observations=normalize_observations,
batch_size=batch_size, action_noise=action_noise,
actor_lr=actor_lr, critic_lr=critic_lr,
)
if USE_CUDA:
agent.cuda()
# Set up logging stuff only for a single worker.
step = 0
episode = 0
episode_rewards_history = deque(maxlen=100)
# Prepare everything.
agent.reset()
obs = env.reset()
done = False
episode_reward = 0.
episode_step = 0
episodes = 0
t = 0
epoch = 0
start_time = time.time()
epoch_episode_rewards = []
epoch_episode_steps = []
epoch_start_time = time.time()
epoch_actions = []
epoch_qs = []
epoch_episodes = 0
for epoch in range(nb_epochs):
for cycle in range(nb_epoch_cycles):
# Perform rollouts.
for t_rollout in range(nb_rollout_steps):
# Predict next action.
action, q = agent.pi(obs, apply_noise=True, compute_Q=True)
assert action.shape == env.action_space.shape
# Execute next action.
assert max_action.shape == action.shape
new_obs, r, done, info = env.step(max_action * action)
t += 1
episode_reward += r
episode_step += 1
# Book-keeping.
epoch_actions.append(action)
epoch_qs.append(q)
agent.store_transition(obs, action, r, new_obs, done)
obs = new_obs
if done:
# Episode done.
epoch_episode_rewards.append(episode_reward)
episode_rewards_history.append(episode_reward)
epoch_episode_steps.append(episode_step)
episode_reward = 0.
episode_step = 0
epoch_episodes += 1
episodes += 1
agent.reset()
obs = env.reset()
# Train.
epoch_actor_losses = []
epoch_critic_losses = []
for t_train in range(nb_train_steps):
cl, al = agent.train()
epoch_critic_losses.append(cl)
epoch_actor_losses.append(al)
agent.update_target_net()
# Log stats.
# XXX shouldn't call np.mean on variable length lists
duration = time.time() - start_time
combined_stats = dict()
combined_stats['rollout/return'] = np.mean(epoch_episode_rewards)
combined_stats['rollout/return_history'] = np.mean(episode_rewards_history)
combined_stats['rollout/episode_steps'] = np.mean(epoch_episode_steps)
combined_stats['rollout/actions_mean'] = np.mean(epoch_actions)
combined_stats['rollout/Q_mean'] = np.mean(epoch_qs)
combined_stats['train/loss_actor'] = np.mean(epoch_actor_losses)
combined_stats['train/loss_critic'] = np.mean(epoch_critic_losses)
combined_stats['total/duration'] = duration
combined_stats['total/steps_per_second'] = float(t) / float(duration)
combined_stats['total/episodes'] = episodes
combined_stats['rollout/episodes'] = epoch_episodes
combined_stats['rollout/actions_std'] = np.std(epoch_actions)
# Total statistics.
combined_stats['total/epochs'] = epoch + 1
combined_stats['total/steps'] = t
for key in sorted(combined_stats.keys()):
logger.record_tabular(key, combined_stats[key])
logger.dump_tabular()
logger.info('')
def main():
# Initialize the ANNs
agent = DDPG()
rospy.init_node("neuro_deep_planner", anonymous=False)
ros_handler = ROSHandler()
ros_handler.on_policy = False
# For plotting
currently_plotting = False
goal_count = 0
crash_count = 0
start_time = 0
# Make sure the directory for the plotting exists
if not tf.gfile.Exists(PLOT_PATH):
tf.gfile.MakeDirs(PLOT_PATH)
f = open(PLOT_PATH + '/results', 'w')
while not rospy.is_shutdown():
# If we are plotting results we don't want to train and need to turn of noise!
if PLOTTING and not currently_plotting and agent.training_step > 0 and \
agent.training_step % PLOT_INTERVALL == 0:
currently_plotting = True
agent.noise_flag = False
start_time = rospy.get_time()
if currently_plotting and rospy.get_time() - start_time > PLOT_TIME:
# Plot the results
string = str(agent.training_step) + ', ' + str(goal_count) + ', ' + str(crash_count) + '\n'
f.write(string)
# Reset all parameters
currently_plotting = False
agent.noise_flag = True
goal_count = 0
crash_count = 0
# If we are plotting results we need to count reached goals and crashes
if currently_plotting:
# Count the positive and negative rewards
if ros_handler.new_msg():
if not ros_handler.is_episode_finished:
# Send back the action to execute
ros_handler.publish_action(agent.get_action(ros_handler.state))
elif ros_handler.reward == 1:
goal_count += 1
elif ros_handler.reward == -1:
crash_count += 1
# If we're not plotting results
else:
# If we have a new msg we might have to execute an action and need to put the new experience in the buffer
if ros_handler.new_msg():
if not ros_handler.is_episode_finished:
# Send back the action to execute
ros_handler.publish_action(agent.get_action(ros_handler.state))
# Safe the past state and action + the reward and new state into the replay buffer
agent.set_experience(ros_handler.state, ros_handler.reward, ros_handler.is_episode_finished)
elif ros_handler.new_setting():
agent.noise_flag = ros_handler.noise_flag
else:
# Train the network!
agent.train()
def main():
experiment= 'InvertedPendulum-v1'
env= gym.make(experiment)
assert isinstance(env.observation_space, Box), "observation space must be continuous"
assert isinstance(env.action_space, Box), "action space must be continuous"
#Randomly initialize critic,actor,target critic, target actor network and replay buffer
agent = DDPG(env)
exploration_noise = OUNoise(env.action_space.shape[0])
counter=0
total_reward=0
num_states = env.observation_space.shape[0]
num_actions = env.action_space.shape[0]
#saving reward:
reward_st = np.array([0])
for i in xrange(episodes):
observation = env.reset()
reward_per_episode = 0
for t in xrange(steps):
#rendering environmet (optional)
#env.render()
x = observation
#select action using actor network model
action = agent.evaluate_actor(np.reshape(x,[num_actions,num_states]))
noise = exploration_noise.noise()
action = action[0] + noise
print 'Agent.Action :',action
print '\n'
print '\n'
observation,reward,done,[]=env.step(action)
#add s_t,s_t+1,action,reward to experience memeroy
agent.add_experience(x,observation,action,reward,done)
#train critic and actor network
if counter > 64:
agent.train()
reward_per_episode+=reward
counter+=1
#check if episode ends:
if done:
print 'EPISODE: ',i,' Steps: ',t,' Total Reward: ',reward_per_episode
exploration_noise.reset()
reward_st = np.append(reward_st,reward_per_episode)
np.savetxt('episode_reward.txt',reward_st, newline="\n")
print '\n'
print '\n'
break
total_reward+=reward_per_episode
print "Average reward per episode {}".format(total_reward / episodes)
parser.add_argument('--replay_size', type=int, default=1000000, metavar='N',
help='size of replay buffer (default: 1000000)')
parser.add_argument('--render', action='store_true',
help='render the environment')
parser.add_argument('--threshold', type=float, default=0.5, metavar='G',
help='threshold for PMV')
args = parser.parse_args()
num_inputs = 4
num_outputs = 2
torch.manual_seed(args.seed)
np.random.seed(args.seed)
action_space = np.array([0.0, 0.0])
agent = DDPG(args.gamma, args.tau, args.hidden_size, num_inputs, action_space)
memory = ReplayMemory(args.replay_size)
ounoise = OUNoise(num_outputs)
db = db_opt.DB()
if db.is_open():
print("the connection is open")
else:
print("the connection is closed")
sys.exit()
step = 1
rewards = []
for i_episode in range(args.num_episodes):
ounoise.scale = (args.noise_scale - args.final_noise_scale) * max(0, args.exploration_end -
