def main():
args = arg_parse_TRPO()
env = gym.make(args.env_name)
env.seed(args.seed)
torch.manual_seed(args.seed)
num_inputs = env.observation_space.shape[0]
num_actions = env.action_space.shape[0]
running_state = ZFilter((num_inputs, ), clip=5)
running_reward = ZFilter((1, ), demean=False, clip=10)
policy_net = Policy(num_inputs, num_actions)
value_net = Value(num_inputs)
train(args, env, policy_net, value_net, running_state)
def __init__(self, env, args):
self.env = env
self.args = args
# define the network
self.net = Network(self.env.observation_space.shape[0],
self.env.action_space.shape[0])
self.old_net = Network(self.env.observation_space.shape[0],
self.env.action_space.shape[0])
# make sure the net and old net have the same parameters
self.old_net.load_state_dict(self.net.state_dict())
# define the optimizer
self.optimizer = torch.optim.Adam(self.net.critic.parameters(),
lr=self.args.lr)
# define the running mean filter
self.running_state = ZFilter((self.env.observation_space.shape[0], ),
clip=5)
if not os.path.exists(self.args.save_dir):
os.mkdir(self.args.save_dir)
self.model_path = self.args.save_dir + self.args.env_name
if not os.path.exists(self.model_path):
os.mkdir(self.model_path)
self.start_episode = 0
def sim_episode(env, policy, max_episode_steps, result_writer):
"""Simulate an episode and store the resulting render."""
try:
running_state = ZFilter((env.observation_space.shape[0], ), clip=5)
state = env.reset()
state = running_state(state)
frames_store = []
for t in range(
max_episode_steps): # Don't infinite loop while learning
# Simulates one episode, i.e., until the agent reaches the terminal state or has taken 10000 steps in the environment
action_mean, action_log_std, action_std = policy(
Variable(torch.Tensor([state])))
action = torch.normal(action_mean,
action_std).detach().data[0].cpu().numpy()
next_state, reward, done, _ = env.step(action)
next_state = running_state(next_state)
state = next_state
frames = env.render('rgb_array')
frames_store.append(frames)
return frames_store
except Exception as e:
print(f'Tried running simulation, but got error:{e}')
return []
def __init__(self, envs, args, net, env_type='atari'):
self.envs = envs
self.args = args
self.env_type = env_type
# define the newtork...
self.net = net
self.old_net = copy.deepcopy(self.net)
# if use the cuda...
if self.args.cuda:
self.net.cuda()
self.old_net.cuda()
# define the optimizer...
self.optimizer = optim.Adam(self.net.parameters(), self.args.lr, eps=self.args.eps)
# running filter...
if self.env_type == 'mujoco':
num_states = self.envs.observation_space.shape[0]
self.running_state = ZFilter((num_states, ), clip=5)
# check saving folder..
if not os.path.exists(self.args.save_dir):
os.mkdir(self.args.save_dir)
# env folder..
self.model_path = os.path.join(self.args.save_dir, self.args.env_name)
if not os.path.exists(self.model_path):
os.mkdir(self.model_path)
# get the observation
self.batch_ob_shape = (self.args.num_workers * self.args.nsteps, ) + self.envs.observation_space.shape
self.obs = np.zeros((self.args.num_workers, ) + self.envs.observation_space.shape, dtype=self.envs.observation_space.dtype.name)
if self.env_type == 'mujoco':
self.obs[:] = np.expand_dims(self.running_state(self.envs.reset()), 0)
else:
self.obs[:] = self.envs.reset()
self.dones = [False for _ in range(self.args.num_workers)]
def sample(self, policy, params=None, gamma=0.95, device='cpu'):
episodes = BatchEpisodes(batch_size=self.batch_size,
gamma=gamma,
device=device)
for i in range(self.batch_size):
self.queue.put(i)
for _ in range(self.num_workers):
self.queue.put(None)
observations, batch_ids = self.envs.reset()
running_state = ZFilter((observations.shape[1], ), clip=5)
for index in range(observations.shape[0]):
observations[index, :] = running_state(observations[index, :])
dones = [False]
while (not all(dones)) or (not self.queue.empty()):
with torch.no_grad():
observations_tensor = torch.from_numpy(observations).to(
device=device)
actions_tensor = policy(observations_tensor,
params=params).sample()
actions = actions_tensor.cpu().numpy()
new_observations, rewards, dones, new_batch_ids, _ = self.envs.step(
actions)
episodes.append(observations, actions, rewards, batch_ids)
observations, batch_ids = new_observations, new_batch_ids
for index in range(observations.shape[0]):
observations[index, :] = running_state(observations[index, :])
return episodes
def __init__(self, env, policy_network, value_network, gamma=0.99, policy_lr=0.0005, value_lr=0.0005, tau=0.95,
value_update_step=10, policy_update_step=10,
epsilon=0.2, batch_size=64, use_cuda=True, saved_path='saved_models/Walker2d-v1/'):
# define the parameters...
self.env = env
self.gamma = gamma
self.policy_lr = policy_lr
self.value_lr = value_lr
self.tau = tau
self.value_update_step = value_update_step
self.policy_update_step = policy_update_step
self.epsilon = epsilon
self.batch_size = batch_size
self.saved_path = saved_path
# check if cuda is avaiable...
self.use_cuda = torch.cuda.is_available() and use_cuda
print('The cuda is avaiable: ' + str(self.use_cuda))
# define the network...
self.policy_network = policy_network
self.value_network = value_network
if self.use_cuda:
self.policy_network.cuda()
self.value_network.cuda()
# define the optimizer
self.optimizer_value = torch.optim.Adam(self.value_network.parameters(), lr=self.value_lr)
self.optimizer_policy = torch.optim.Adam(self.policy_network.parameters(), lr=self.policy_lr)
# init the Filter...
self.running_state = ZFilter((self.env.observation_space.shape[0],), clip=5)
def __init__(self, env, policy_lr, value_lr, tau, gamma, buffer_size,
max_time_step, observate_time, batch_size, path,
soft_update_step, use_cuda):
self.env = env
self.policy_lr = policy_lr
self.value_lr = value_lr
self.use_cuda = bool(use_cuda)
self.tau = tau
self.gamma = gamma
self.buffer_size = buffer_size
self.max_time_step = max_time_step
self.observate_time = observate_time
self.batch_size = batch_size
self.global_time_step = 0
self.path = path
self.soft_update_step = soft_update_step
print('IF USE CUDA: ' + str(self.use_cuda))
num_inputs = self.env.observation_space.shape[0]
self.num_actions = self.env.action_space.shape[0]
# the scale of the action space....
self.action_scale = self.env.action_space.high[0]
# build up the network....
# build the actor_network firstly...
self.actor_net = models.Policy(num_inputs, self.num_actions)
self.actor_target_net = models.Policy(num_inputs, self.num_actions)
# build the critic_network....
self.critic_net = models.Critic(num_inputs, self.num_actions)
self.critic_target_net = models.Critic(num_inputs, self.num_actions)
# if use cuda...
if self.use_cuda:
self.actor_net.cuda()
self.actor_target_net.cuda()
self.critic_net.cuda()
self.critic_target_net.cuda()
# init the same parameters....
self.actor_target_net.load_state_dict(self.actor_net.state_dict())
self.critic_target_net.load_state_dict(self.critic_net.state_dict())
# define the optimize.... add the L2 reg in critic optimzier here...
self.optimizer_actor = torch.optim.Adam(self.actor_net.parameters(),
lr=self.policy_lr)
self.optimizer_critic = torch.optim.Adam(self.critic_net.parameters(),
lr=self.value_lr,
weight_decay=1e-2)
# init the filter...
self.running_state = ZFilter((num_inputs, ), clip=5)
def __init__(self, args, env):
# define the arguments and environments...
self.args = args
self.env = env
# define the num of inputs and num of actions
num_inputs = self.env.observation_space.shape[0]
num_actions = self.env.action_space.shape[0]
# define the model save dir...
self.saved_path = self.args.save_dir + self.args.env_name + '/'
# check the path
if not os.path.exists(self.args.save_dir):
os.mkdir(self.args.save_dir)
if not os.path.exists(self.saved_path):
os.mkdir(self.saved_path)
# define the networks...
self.policy_network = models.Policy(num_inputs, num_actions)
self.value_network = models.Value(num_inputs)
# define the optimizer
self.optimizer_value = torch.optim.Adam(self.value_network.parameters(), lr=self.args.value_lr, weight_decay=self.args.l2_reg)
# init the filter...
self.running_state = ZFilter((num_inputs,), clip=5)
def __init__(self, env, args):
# define the parameters...
self.env = env
# get the environment's input size and output size
num_inputs = self.env.observation_space.shape[0]
num_actions = self.env.action_space.shape[0]
# get the parameters
self.args = args
self.saved_path = 'saved_models/' + str(self.args.env_name) + '/'
# check the path
if not os.path.exists(self.saved_path):
os.mkdir(self.saved_path)
# check if cuda is avaiable...
self.use_cuda = torch.cuda.is_available() and self.args.cuda
print('The cuda is avaiable: ' + str(torch.cuda.is_available()))
print('If use the cuda: ' + str(self.args.cuda))
# define the network...
self.policy_network = models.Policy(num_inputs, num_actions)
self.value_network = models.Value(num_inputs)
if self.use_cuda:
self.policy_network.cuda()
self.value_network.cuda()
# define the optimizer
self.optimizer_value = torch.optim.Adam(
self.value_network.parameters(),
lr=self.args.value_lr,
weight_decay=self.args.l2_reg)
self.optimizer_policy = torch.optim.Adam(
self.policy_network.parameters(),
lr=self.args.policy_lr,
weight_decay=self.args.l2_reg)
# init the Filter...
self.running_state = ZFilter((num_inputs, ), clip=5)
return action_loss.mean()
def get_kl():
mean1, log_std1, std1 = policy_net(Variable(states))
mean0 = Variable(mean1.data)
log_std0 = Variable(log_std1.data)
std0 = Variable(std1.data)
kl = log_std1 - log_std0 + (std0.pow(2) + (mean0 - mean1).pow(2)) / (
2.0 * std1.pow(2)) - 0.5
return kl.sum(1, keepdim=True)
trpo_step(policy_net, get_loss, get_kl, args.max_kl, args.damping)
running_state = ZFilter((num_inputs, ), clip=5)
running_reward = ZFilter((1, ), demean=False, clip=10)
for i_episode in count(1):
memory = Memory()
num_steps = 0
reward_batch = 0
num_episodes = 0
while num_steps < args.batch_size:
state = env.reset()
state = running_state(state)
reward_sum = 0
for t in range(10000): # Don't infinite loop while learning
action = select_action(state)
def main(gamma=0.995, env_name='Walker2d-v2', tau=0.97, seed=543, number_of_batches=500,\
batch_size=5000, maximum_steps=10000, render=False, log_interval=1, entropy_coeff=0.0,\
clip_epsilon=0.2, use_joint_pol_val=False):
torch.set_default_tensor_type('torch.DoubleTensor')
PI = torch.DoubleTensor([3.1415926])
env = gym.make(env_name)
num_inputs = env.observation_space.shape[0]
num_actions = env.action_space.shape[0]
env.seed(seed)
torch.manual_seed(seed)
policy_net = Policy(num_inputs, num_actions)
value_net = Value(num_inputs)
opt_policy = optim.Adam(policy_net.parameters(), lr=0.001)
opt_value = optim.Adam(value_net.parameters(), lr=0.001)
running_state = ZFilter((num_inputs,), clip=5)
running_reward = ZFilter((1,), demean=False, clip=10)
episode_lengths = []
plot_rew = []
for i_episode in range(number_of_batches):
memory = Memory()
num_steps = 0
reward_batch = 0
num_episodes = 0
while num_steps < batch_size:
state = env.reset()
state = running_state(state)
reward_sum = 0
for t in range(maximum_steps): # Don't infinite loop while learning
action = select_action(state, policy_net)
action = action.data[0].numpy()
next_state, reward, done, _ = env.step(action)
reward_sum += reward
next_state = running_state(next_state)
mask = 1
if done:
mask = 0
memory.push(state, np.array([action]), mask, next_state, reward)
if render:
env.render()
if done:
break
state = next_state
num_steps += (t-1)
num_episodes += 1
reward_batch += reward_sum
reward_batch /= num_episodes
batch = memory.sample()
plot_rew.append(reward_batch)
update_params(batch, policy_net, value_net, gamma, opt_policy, opt_value)
if i_episode % args.log_interval == 0:
print('Episode {}\tLast reward: {}\tAverage reward {:.2f}'.format(
i_episode, reward_sum, reward_batch))
plot_epi = []
for i in range (number_of_batches):
plot_epi.append(i)
trace = go.Scatter( x = plot_epi, y = plot_rew)
layout = go.Layout(title='PPO',xaxis=dict(title='Episodes', titlefont=dict(family='Courier New, monospace',size=18,color='#7f7f7f')),
yaxis=dict(title='Average Reward', titlefont=dict(family='Courier New, monospace',size=18,color='#7f7f7f')))
plotly.offline.plot({"data": [trace], "layout": layout},filename='PPO.html',image='jpeg')
def main(gamma=0.995, env_name="Walker2d-v2", tau=0.97, number_of_batches=500,\
batch_size=5000, maximum_steps=10000, render=False,\
seed=543, log_interval=1, entropy_coeff=0.0, clip_epsilon=0.2):
env = gym.make(env_name)
#Get number of inputs for A3CActor
num_inputs = env.observation_space.shape[0]
#Get number of outputs required for describing action
num_actions = env.action_space.shape[0]
env.seed(seed)
torch.manual_seed(seed)
actor_net = A3CActor(num_inputs, num_actions)
actor_optimizer = optim.Adam(actor_net.parameters(), lr=0.001)
running_state = ZFilter((num_inputs, ), clip=5)
running_reward = ZFilter((1, ), demean=False, clip=10)
episode_lengths = []
for i_episode in range(number_of_batches):
memory = Memory()
num_steps = 0
reward_batch = 0
num_episodes = 0
while num_steps < batch_size:
state = env.reset()
state = running_state(state)
reward_sum = 0
for t in range(maximum_steps):
action = select_action(state, actor_net)
action = action.data[0].numpy()
next_state, reward, done, _ = env.step(action)
reward_sum += reward
next_state = running_state(next_state)
mask = 1
if done:
mask = 0
memory.push(state, np.array([action]), mask, next_state,
reward)
if render:
env.render()
if done:
break
state = next_state
num_steps += (t - 1)
num_episodes += 1
reward_batch += reward_sum
reward_batch /= num_episodes
batch = memory.sample()
update_params(batch, actor_net, actor_optimizer, gamma, tau,
clip_epsilon)
if i_episode % log_interval == 0:
print('Episode {}\t Last reward: {}\tAverage reward {:.2f}'.format(
i_episode, reward_sum, reward_batch))
return
policy_net = Observations_Encoder(rows,
cols,
3,
z_dim,
num_goals,
num_actions,
threshold,
device=device).to(device)
policy_net.train()
#### Initializing Environment
env = env_BP_w_display(num_goals)
#### this is a low pass filter which improves training
running_state = ZFilter(((3, rows, cols)), clip=5)
running_reward = ZFilter((1, ), demean=False, clip=10)
if model_path != "":
print("MODEL LOADED")
ckpt = torch.load(model_path)
policy_net.load_state_dict(ckpt['policy_net'])
value_net.load_state_dict(ckpt['value_net'])
running_state.rs._M = ckpt['running_M']
running_state.rs._S = ckpt['running_S']
running_state.rs._n = ckpt['running_n']
else:
print("NO RL MODEL LOADED")
##### Logging Folders #####
def test():
env_id="MotorEnv-v0"
env = gym.make(env_id) #创造环境
num_inputs = env.observation_space.shape[0]
net = torch.load('./models/model6.pkl')
print(net)
with torch.no_grad():
running_state = ZFilter((num_inputs,), clip=5)
episodes =[]
eval_rewards =[]
eval_done = []
eval_states = []
eval_input = []
eval_delta = []
eval_L = []
state = env.reset()
state = running_state(state)
render = False
for t in range(int(50 / env.steps)):
# action = env.action_space.sample() #随机采样动作
# observation, reward, done, info = env.step(1) #与环境交互，获得下一步的时刻
action = select_action(state, net) #action
action = action.data[0].numpy()
observation, reward, done, info = env.step(action) #与环境交互，获得下一步的时刻
state = running_state(observation)
C = np.array([0, 1, 0, 0])
L = np.dot(C, observation.reshape(4,1))
# if done:
# break
# pass
# env.render() #绘制场景
# count+=1
# time.sleep(0.001) #每次等待0.2s
# print(info['input'],env.state, env.counts)
# print(env.counts)
episodes.append(env.counts)
eval_states.append(observation)
eval_rewards.append(reward)
eval_done.append(done)
eval_L.append(L)
eval_input.append(info['input'])
eval_delta.append(info['delta'])
episodes = np.array(episodes)
eval_rewards = np.array(eval_rewards)
eval_states = np.array(eval_states)
eval_done = np.array(eval_done)
eval_input = np.array(eval_input)
eval_delta = np.array(eval_delta)
fig = plt.figure("VibrationEnv-states")
plt.plot(episodes, eval_L)
plt.title("%s"%env_id)
plt.xlabel("Episode")
plt.ylabel("eval_states")
plt.legend(["x","y","p","q"])
plt.grid()
plt.show()
fig = plt.figure("VibrationEnv-u")
plt.plot(episodes, eval_input)
plt.title("%s"%env_id)
plt.xlabel("Episode")
plt.ylabel("eval_states")
plt.legend(["u"])
plt.grid()
plt.show()
env.close()
value_net = Value(num_inputs)
summary_writer = tensorboardX.SummaryWriter(log_dir)
def select_action(state, deterministic=False):
state = torch.from_numpy(state).unsqueeze(0)
action_mean, _, action_std = policy_net(Variable(state))
if not deterministic:
action = torch.normal(action_mean, action_std)
else:
action = action_mean # action is mode
return action
running_state = ZFilter((num_inputs, ), clip=5)
ckpt = torch.load(args.checkpoint)
policy_net.load_state_dict(ckpt['policy_net'])
value_net.load_state_dict(ckpt['value_net'])
running_state.rs._M = ckpt['running_M']
running_state.rs._S = ckpt['running_S']
running_state.rs._n = ckpt['running_n']
eval_hole = []
eval_rewards = []
eval_completed = []
eval_touched = []
for eval_episode in range(args.eval_eps):
def test(rank, args, shared_model, opt_ac):
best_result = -1000
torch.manual_seed(args.seed + rank)
torch.set_default_tensor_type('torch.DoubleTensor')
num_inputs = args.feature
num_actions = 9
last_state = numpy.zeros(41)
if args.render:
env = RunEnv(visualize=True)
else:
env = RunEnv(visualize=False)
running_state = ZFilter((num_inputs, ), clip=5)
running_reward = ZFilter((1, ), demean=False, clip=10)
episode_lengths = []
PATH_TO_MODEL = '../models/' + str(args.bh)
ac_net = ActorCritic(num_inputs, num_actions)
start_time = time.time()
for i_episode in count(1):
memory = Memory()
ac_net.load_state_dict(shared_model.state_dict())
num_steps = 0
reward_batch = 0
num_episodes = 0
while num_steps < args.batch_size:
#state = env.reset()
#print(num_steps)
state = env.reset(difficulty=0)
state = numpy.array(state)
#global last_state
#last_state = state
#last_state,_ = update_observation(last_state,state)
#last_state,state = update_observation(last_state,state)
#print(state.shape[0])
#print(state[41])
state = running_state(state)
reward_sum = 0
for t in range(10000): # Don't infinite loop while learning
#print(t)
#timer = time.time()
if args.use_sep_pol_val:
action = select_action(state)
else:
action = select_action_actor_critic(state, ac_net)
#print(action)
action = action.data[0].numpy()
if numpy.any(numpy.isnan(action)):
print(action)
puts('ERROR')
return
#print('NN take:')
#print(time.time()-timer)
#print(action)
#print("------------------------")
#timer = time.time()
if args.skip:
#env.step(action)
_, reward, _, _ = env.step(action)
reward_sum += reward
next_state, reward, done, _ = env.step(action)
next_state = numpy.array(next_state)
reward_sum += reward
#print('env take:')
#print(time.time()-timer)
#timer = time.time()
#last_state ,next_state = update_observation(last_state,next_state)
next_state = running_state(next_state)
#print(next_state[41:82])
mask = 1
if done:
mask = 0
#print('update take:')
#print(time.time()-timer)
#timer = time.time()
memory.push(state, np.array([action]), mask, next_state,
reward)
#print('memory take:')
#print(time.time()-timer)
#if args.render:
# env.render()
if done:
break
state = next_state
num_steps += (t - 1)
num_episodes += 1
#print(num_episodes)
reward_batch += reward_sum
#print(num_episodes)
reward_batch /= num_episodes
batch = memory.sample()
#update_params_actor_critic(batch,args,shared_model,ac_net,opt_ac)
time.sleep(60)
if i_episode % args.log_interval == 0:
File = open(PATH_TO_MODEL + '/record.txt', 'a+')
File.write("Time {}, episode reward {}, Average reward {}".format(
time.strftime("%Hh %Mm %Ss",
time.gmtime(time.time() - start_time)),
reward_sum, reward_batch))
File.close()
#print('TestEpisode {}\tLast reward: {}\tAverage reward {:.2f}'.format(
# i_episode, reward_sum, reward_batch))
print("Time {}, episode reward {}, Average reward {}".format(
time.strftime("%Hh %Mm %Ss",
time.gmtime(time.time() - start_time)),
reward_sum, reward_batch))
#print('!!!!')
epoch = i_episode
if reward_batch > best_result:
best_result = reward_batch
save_model(
{
'epoch': epoch,
'bh': args.bh,
'state_dict': shared_model.state_dict(),
'optimizer': opt_ac.state_dict(),
}, PATH_TO_MODEL, 'best')
if epoch % 30 == 1:
save_model(
{
'epoch': epoch,
'bh': args.bh,
'state_dict': shared_model.state_dict(),
'optimizer': opt_ac.state_dict(),
}, PATH_TO_MODEL, epoch)
def main(gamma=0.995, env_name="Walker2d-v2", tau=0.97, number_of_batches=500,\
batch_size=5000, maximum_steps=10000, render=False,\
seed=543, log_interval=1, entropy_coeff=0.0, clip_epsilon=0.2):
env = gym.make(env_name)
#Get number of inputs for A3CActor
num_inputs = env.observation_space.shape[0]
#Get number of outputs required for describing action
num_actions = env.action_space.shape[0]
env.seed(seed)
torch.manual_seed(seed)
actor_net = A3CActor(num_inputs, num_actions)
actor_optimizer = optim.Adam(actor_net.parameters(), lr=0.001)
running_state = ZFilter((num_inputs,), clip=5)
running_reward = ZFilter((1, ), demean=False, clip=10)
episode_lengths = []
plot_rew = []
for i_episode in range(number_of_batches):
memory = Memory()
num_steps = 0
reward_batch = 0
num_episodes = 0
while num_steps < batch_size:
state = env.reset()
state = running_state(state)
reward_sum = 0
for t in range(maximum_steps):
action = select_action(state, actor_net)
action = action.data[0].numpy()
next_state, reward, done, _ = env.step(action)
reward_sum += reward
next_state = running_state(next_state)
mask = 1
if done:
mask = 0
memory.push(state, np.array([action]), mask, next_state, reward)
if render:
env.render()
if done:
break
state = next_state
num_steps += (t-1)
num_episodes += 1
reward_batch += reward_sum
reward_batch /= num_episodes
batch = memory.sample()
plot_rew.append(reward_batch)
update_params(batch, actor_net, actor_optimizer, gamma, tau, clip_epsilon)
if i_episode % log_interval == 0:
print('Episode {}\t Last reward: {}\tAverage reward {:.2f}'.format(
i_episode, reward_sum, reward_batch))
plot_epi = []
for i in range (number_of_batches):
plot_epi.append(i)
trace = go.Scatter( x = plot_epi, y = plot_rew)
layout = go.Layout(title='A2C',xaxis=dict(title='Episodes', titlefont=dict(family='Courier New, monospace',size=18,color='#7f7f7f')),
yaxis=dict(title='Average Reward', titlefont=dict(family='Courier New, monospace',size=18,color='#7f7f7f')))
plotly.offline.plot({"data": [trace], "layout": layout},filename='PPO.html',image='jpeg')
return
