def train(args, env, policy_net, value_net, running_state):
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, 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 args.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()
#########################
# TRPO update parameters
#########################
update_trpo = trpo.update_params(batch, value_net, policy_net, args,
trpo_functions)
update_trpo.execute()
if i_episode % args.log_interval == 0:
print('Episode {}\tLast reward: {}\tAverage reward {:.2f}'.format(
i_episode, reward_sum, reward_batch))
def collect_samples(policy_net, min_batch_size):
memory = Memory()
num_steps = 0
reward_batch = 0
num_episodes = 0
while (num_steps < min_batch_size):
state = env.reset()
reward_sum = 0
for t in range(10000):
action = select_action(policy_net, state)
action = action.data[0].numpy()
next_state, reward, done, _ = env.step(action)
reward_sum += reward
mask = 0 if done else 1
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
print(num_episodes)
reward_batch = reward_batch / num_episodes
batch = memory.sample()
return batch, reward_batch
if done:
mask = 0
mem_mask.append(mask)
mem_next.append(next_state)
if done:
break
state = next_state
num_steps += (t - 1)
num_episodes += 1
reward_batch.append(reward_sum)
tb_writer.add_scalar('Mean Episode Reward',
np.sum(reward_batch) / len(reward_batch), episode)
evaluate(episode)
rewards = expert_reward(states, actions)
for idx in range(len(states)):
memory.push(states[idx][0], actions[idx], mem_mask[idx],
mem_next[idx], rewards[idx][0])
batch = memory.sample()
update_params(batch)
# ce_train_discriminator_one_step(gen_state_action_batch, expert_state_action_batch, expert_pvalue)
actions = torch.from_numpy(np.concatenate(actions))
states = torch.from_numpy(np.concatenate(states))
idx = np.random.randint(0, expert_traj.shape[0], num_steps)
expert_state_action = expert_traj[idx, :]
expert_pvalue = expert_conf[idx, :]
expert_state_action = torch.Tensor(expert_state_action).to(device)
expert_pvalue = torch.Tensor(expert_pvalue / Z).to(device)
state_action = torch.cat((states, actions), 1).to(device)
ce_loss = ce_train_discriminator_one_step(state_action,
expert_state_action,
expert_pvalue)
def train(rank, args, traffic_light, counter, shared_model,
shared_grad_buffers, shared_obs_stats, 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 = [0] * 41
last_v = [0] * 10
#last_state = numpy.zeros(48)
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)
#running_state = ZFilter((num_inputs,), clip=5)
start_time = time.time()
for i_episode in range(args.start_epoch + 1, 999999):
#print(shared_obs_stats.n[0])
#print('hei')
#if rank == 0:
# print(running_state.rs._n)
signal_init = traffic_light.get()
memory = Memory()
ac_net.load_state_dict(shared_model.state_dict())
num_steps = 0
reward_batch = 0
num_episodes = 0
#Tot_loss = 0
#Tot_num =
while num_steps < args.batch_size:
#state = env.reset()
#print(num_steps)
state = env.reset(difficulty=0)
#state = numpy.array(state)
last_state, last_v, state = process_observation(
last_state, last_v, state)
state = numpy.array(state)
#state = running_state(state)
state = Variable(torch.Tensor(state).unsqueeze(0))
shared_obs_stats.observes(state)
state = shared_obs_stats.normalize(state)
state = state.data[0].numpy()
#print(state)
#return
#print(AA)
#print(type(AA))
#print(type(state))
#print(AA.shape)
#print(state.shape)
reward_sum = 0
#timer = time.time()
for t in range(10000): # Don't infinite loop while learning
#print(t)
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(state)
print(action)
print(ac_net.affine1.weight)
print(ac_net.affine1.weight.data)
print('ERROR')
#action = select_action_actor_critic(state,ac_net)
#action = action.data[0].numpy()
#state = state + numpy.random.rand(args.feature)*0.001
raise RuntimeError('action NaN problem')
#print(action)
#print("------------------------")
#timer = time.time()
reward = 0
if args.skip:
#env.step(action)
_, A, _, _ = env.step(action)
reward += A
_, A, _, _ = env.step(action)
reward += A
BB = numpy.append(action, action)
next_state, A, done, _ = env.step(BB)
reward += A
#print(next_state)
#last_state = process_observation(state)
last_state, last_v, next_state = process_observation(
last_state, last_v, next_state)
next_state = numpy.array(next_state)
#print(next_state)
#print(next_state.shape)
#return
reward_sum += reward
#print('env:')
#print(time.time()-timer)
#last_state ,next_state = update_observation(last_state,next_state)
#next_state = running_state(next_state)
next_state = Variable(torch.Tensor(next_state).unsqueeze(0))
shared_obs_stats.observes(next_state)
next_state = shared_obs_stats.normalize(next_state)
next_state = next_state.data[0].numpy()
#print(next_state[41:82])
mask = 1
if done:
mask = 0
memory.push(state, np.array([action]), mask, next_state,
reward)
#if args.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()
#print('env:')
#print(time.time()-timer)
#timer = time.time()
update_params_actor_critic(batch, args, ac_net, opt_ac)
shared_grad_buffers.add_gradient(ac_net)
counter.increment()
epoch = i_episode
if (i_episode % args.log_interval == 0) and (rank == 0):
print(
'TrainEpisode {}\tTime{}\tLast reward: {}\tAverage reward {:.2f}'
.format(
i_episode,
time.strftime("%Hh %Mm %Ss",
time.gmtime(time.time() - start_time)),
reward_sum, reward_batch))
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(),
'obs': shared_obs_stats,
}, 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(),
'obs': shared_obs_stats,
}, PATH_TO_MODEL, epoch)
# wait for a new signal to continue
while traffic_light.get() == signal_init:
pass
state = running_state(state)
reward_sum = 0
for t in range(10000): # Don't infinite loop while learning
action = select_action(state)
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 args.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)
def collect_samples(pid, queue, env, policy, encoder, render, running_state,
custom_reward, min_batch_size):
torch.set_num_threads(1)
if pid > 0:
torch.manual_seed(torch.randint(0, 5000, (1, )) * pid)
if hasattr(env, 'np_random'):
env.np_random.seed(env.np_random.randint(5000) * pid)
if hasattr(env, 'env') and hasattr(env.env, 'np_random'):
env.env.np_random.seed(env.env.np_random.randint(5000) * pid)
log = dict()
memory = Memory()
num_steps = 0
num_episodes = 0
min_episode_reward = float('inf')
max_episode_reward = float('-inf')
total_reward = 0
while num_steps < min_batch_size:
state = env.reset()
episode_reward = 0
if running_state:
state = running_state(state)
for t in range(10000):
if render:
env.render()
enco_state = FLOAT(state).unsqueeze(0) #.to(device)
with torch.no_grad():
enco_state = encoder.sample_prediction(enco_state)
enco_state = enco_state.cpu().numpy()[0]
state_tensor = FLOAT(enco_state).unsqueeze(0)
with torch.no_grad():
action, log_prob = policy.get_action_log_prob(state_tensor)
action = action.cpu().numpy()[0]
log_prob = log_prob.cpu().numpy()[0]
next_state, reward, done, _ = env.step(action)
if custom_reward:
reward = custom_reward(state, action)
episode_reward += reward
if running_state:
next_state = running_state(next_state)
mask = 0 if done else 1
# ('state', 'action', 'reward', 'next_state', 'mask', 'log_prob')
memory.push(state, action, reward, next_state, mask, log_prob)
num_steps += 1
if done or num_steps >= min_batch_size:
break
state = next_state
# num_steps += (t + 1)
num_episodes += 1
total_reward += episode_reward
min_episode_reward = min(episode_reward, min_episode_reward)
max_episode_reward = max(episode_reward, max_episode_reward)
log['num_steps'] = num_steps
log['num_episodes'] = num_episodes
log['total_reward'] = total_reward
log['avg_reward'] = total_reward / num_episodes
log['max_episode_reward'] = max_episode_reward
log['min_episode_reward'] = min_episode_reward
if queue is not None:
queue.put([pid, memory, log])
else:
return memory, log
class TD3:
def __init__(self,
env_id,
render=False,
num_process=1,
memory_size=1000000,
lr_p=1e-3,
lr_v=1e-3,
gamma=0.99,
polyak=0.995,
action_noise=0.1,
target_action_noise_std=0.2,
target_action_noise_clip=0.5,
explore_size=10000,
step_per_iter=3000,
batch_size=100,
min_update_step=1000,
update_step=50,
policy_update_delay=2,
seed=1,
model_path=None):
self.env_id = env_id
self.gamma = gamma
self.polyak = polyak
self.action_noise = action_noise
self.target_action_noise_std = target_action_noise_std
self.target_action_noise_clip = target_action_noise_clip
self.memory = Memory(memory_size)
self.explore_size = explore_size
self.step_per_iter = step_per_iter
self.render = render
self.num_process = num_process
self.lr_p = lr_p
self.lr_v = lr_v
self.batch_size = batch_size
self.min_update_step = min_update_step
self.update_step = update_step
self.policy_update_delay = policy_update_delay
self.model_path = model_path
self.seed = seed
self._init_model()
def _init_model(self):
"""init model from parameters"""
self.env, env_continuous, num_states, self.num_actions = get_env_info(
self.env_id)
assert env_continuous, "TD3 is only applicable to continuous environment !!!!"
self.action_low, self.action_high = self.env.action_space.low[
0], self.env.action_space.high[0]
# seeding
np.random.seed(self.seed)
torch.manual_seed(self.seed)
self.env.seed(self.seed)
self.policy_net = Policy(num_states, self.num_actions,
self.action_high).to(device)
self.policy_net_target = Policy(num_states, self.num_actions,
self.action_high).to(device)
self.value_net_1 = Value(num_states, self.num_actions).to(device)
self.value_net_target_1 = Value(num_states,
self.num_actions).to(device)
self.value_net_2 = Value(num_states, self.num_actions).to(device)
self.value_net_target_2 = Value(num_states,
self.num_actions).to(device)
self.running_state = ZFilter((num_states, ), clip=5)
self.num_states = num_states
if self.model_path:
print("Loading Saved Model {}_td3.p".format(self.env_id))
self.policy_net, self.value_net_1, self.value_net_2, self.running_state = pickle.load(
open('{}/{}_td3.p'.format(self.model_path, self.env_id), "rb"))
self.policy_net_target.load_state_dict(self.policy_net.state_dict())
self.value_net_target_1.load_state_dict(self.value_net_1.state_dict())
self.value_net_target_2.load_state_dict(self.value_net_2.state_dict())
self.optimizer_p = optim.Adam(self.policy_net.parameters(),
lr=self.lr_p)
self.optimizer_v_1 = optim.Adam(self.value_net_1.parameters(),
lr=self.lr_v)
self.optimizer_v_2 = optim.Adam(self.value_net_2.parameters(),
lr=self.lr_v)
def choose_action(self, state, noise_scale):
"""select action"""
state = FLOAT(state).unsqueeze(0).to(device)
with torch.no_grad():
action, log_prob = self.policy_net.get_action_log_prob(state)
action = action.cpu().numpy()[0]
# add noise
noise = noise_scale * np.random.randn(self.num_actions)
action += noise
action = np.clip(action, -self.action_high, self.action_high)
return action
def eval(self, i_iter, render=False):
"""evaluate model"""
state = self.env.reset()
test_reward = 0
while True:
if render:
self.env.render()
action = self.choose_action(state, 0)
state, reward, done, _ = self.env.step(action)
test_reward += reward
if done:
break
print(f"Iter: {i_iter}, test Reward: {test_reward}")
self.env.close()
def learn(self, writer, i_iter):
"""interact"""
global_steps = (i_iter - 1) * self.step_per_iter
log = dict()
num_steps = 0
num_episodes = 0
total_reward = 0
min_episode_reward = float('inf')
max_episode_reward = float('-inf')
while num_steps < self.step_per_iter:
state = self.env.reset()
episode_reward = 0
for t in range(10000):
if self.render:
self.env.render()
if global_steps < self.explore_size: # explore
action = self.env.action_space.sample()
else: # action with noise
action = self.choose_action(state, self.action_noise)
next_state, reward, done, _ = self.env.step(action)
# next_state = self.running_state(next_state)
mask = 0 if done else 1
# ('state', 'action', 'reward', 'next_state', 'mask', 'log_prob')
self.memory.push(state, action, reward, next_state, mask, None)
episode_reward += reward
global_steps += 1
num_steps += 1
if global_steps >= self.min_update_step and global_steps % self.update_step == 0:
for k in range(self.update_step):
batch, permuted_batch = self.memory.sample(
self.batch_size) # random sample batch
self.update(batch, permuted_batch, k)
if done or num_steps >= self.step_per_iter:
break
state = next_state
num_episodes += 1
total_reward += episode_reward
min_episode_reward = min(episode_reward, min_episode_reward)
max_episode_reward = max(episode_reward, max_episode_reward)
self.env.close()
log['num_steps'] = num_steps
log['num_episodes'] = num_episodes
log['total_reward'] = total_reward
log['avg_reward'] = total_reward / num_episodes
log['max_episode_reward'] = max_episode_reward
log['min_episode_reward'] = min_episode_reward
print(
f"Iter: {i_iter}, num steps: {log['num_steps']}, total reward: {log['total_reward']: .4f}, "
f"min reward: {log['min_episode_reward']: .4f}, max reward: {log['max_episode_reward']: .4f}, "
f"average reward: {log['avg_reward']: .4f}")
# record reward information
writer.add_scalar("rewards/total_reward", log['total_reward'], i_iter)
writer.add_scalar("rewards/average_reward", log['avg_reward'], i_iter)
writer.add_scalar("rewards/min_reward", log['min_episode_reward'],
i_iter)
writer.add_scalar("rewards/max_reward", log['max_episode_reward'],
i_iter)
writer.add_scalar("rewards/num_steps", log['num_steps'], i_iter)
def update(self, batch, batch2, k_iter):
"""learn model"""
batch_state = FLOAT(batch.state).to(device)
batch_action = FLOAT(batch.action).to(device)
batch_reward = FLOAT(batch.reward).to(device)
batch_next_state = FLOAT(batch.next_state).to(device)
batch_mask = FLOAT(batch.mask).to(device)
# update by TD3
alg_step_stats = td3_step(
self.policy_net, self.policy_net_target, self.value_net_1,
self.value_net_target_1, self.value_net_2, self.value_net_target_2,
self.optimizer_p, self.optimizer_v_1, self.optimizer_v_2,
batch_state, batch_action, batch_reward, batch_next_state,
batch_mask, self.gamma, self.polyak, self.target_action_noise_std,
self.target_action_noise_clip, self.action_high,
k_iter % self.policy_update_delay == 0)
def save(self, save_path):
"""save model"""
check_path(save_path)
pickle.dump((self.policy_net, self.value_net_1, self.value_net_2,
self.running_state),
open('{}/{}_td3.p'.format(save_path, self.env_id), 'wb'))
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')
class Agent:
def __init__(self, n_bits, lr, memory_size, batch_size, gamma):
self.n_bits = n_bits
self.lr = lr
self.gamma = gamma
self.batch_size = batch_size
self.memory_size = memory_size
self.memory = Memory(self.memory_size)
self.device = "cpu"
self.model = DQN(n_inputs=2 * self.n_bits, n_outputs=n_bits).to(self.device)
self.target_model = DQN(n_inputs=2 * self.n_bits, n_outputs=n_bits).to(self.device)
self.target_model.load_state_dict(self.model.state_dict())
self.target_model.eval()
self.opt = Adam(self.model.parameters(), lr=self.lr)
self.loss_fn = MSELoss()
self.epsilon = 1.0
self.epsilon_decay = 0.001
def choose_action(self, states, goals):
if np.random.random() < self.epsilon:
action = np.random.randint(low=0, high=self.n_bits)
else:
states = torch.Tensor(states, device=self.device)
goals = torch.Tensor(goals, device=self.device)
action = self.model(states, goals).max(dim=-1)[1].item()
return action
def update_epsilon(self):
self.epsilon = max(self.epsilon - self.epsilon_decay, 0)
def store(self, state, action, reward, done, next_state, goal):
state = torch.Tensor(state, device=self.device)
reward = torch.Tensor([reward], device=self.device)
action = torch.Tensor([action], device=self.device)
next_state = torch.Tensor(next_state, device=self.device)
done = torch.Tensor([done], device=self.device)
goal = torch.Tensor(goal, device=self.device)
self.memory.push(state, action, reward, done, next_state, goal)
def unpack_batch(self, batch):
batch = Transition(*zip(*batch))
states = torch.cat(batch.state).to(self.device).view(self.batch_size, self.n_bits)
actions = torch.cat(batch.action).to(self.device).view((-1, 1))
rewards = torch.cat(batch.reward).to(self.device)
next_states = torch.cat(batch.next_state).to(self.device).view(self.batch_size, self.n_bits)
dones = torch.cat(batch.done).to(self.device)
goals = torch.cat(batch.goal).to(self.device).view(self.batch_size, self.n_bits)
return states, actions, rewards, dones, next_states, goals
def learn(self):
if len(self.memory) < self.batch_size:
return 0
batch = self.memory.sample(self.batch_size)
states, actions, rewards, dones, next_states, goals = self.unpack_batch(batch)
with torch.no_grad():
target_q = rewards + self.gamma * self.target_model(next_states, goals).max(-1)[0] * (1 - dones)
q = self.model(states, goals).gather(1, actions.long())
loss = self.loss_fn(q, target_q.view(self.batch_size, 1))
self.opt.zero_grad()
loss.backward()
self.opt.step()
self.soft_update_of_target_network(self.model, self.target_model)
return loss.item()
@staticmethod
def soft_update_of_target_network(local_model, target_model, tau=0.05):
for target_param, local_param in zip(target_model.parameters(), local_model.parameters()):
target_param.data.copy_(tau * local_param.data + (1.0 - tau) * target_param.data)
action_pro = select_action(policy_net_pro, value_net_pro, state)
action_pro = action_pro.data[0].numpy()
action_adv = select_action(policy_net_adv, value_net_adv, state)
action_adv = action_adv.data[0].numpy()
action_sum = action_pro + 0.01 * action_adv # can take mean/random choice also
next_state, reward, done, _ = env.step(action_sum)
reward = np.max([-1500.0, reward]) # clip rewards to -1500
reward_sum += reward
next_state = running_state(next_state)
mask = 1
if done:
mask = 0
memory_pro.push(state, np.array([action_pro]), mask, next_state,
reward)
memory_adv.push(state, np.array([action_adv]), mask, next_state,
-1000.0 * reward)
if args.render:
env.render()
if done:
break
state = next_state
num_steps += (t - 1)
num_episodes += 1
reward_batch += reward_sum
reward_batch /= num_episodes
train_interval = i_episode % gan_interval
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
reward = -math.log(reward_net(torch.cat((Variable(torch.from_numpy(s.state).unsqueeze(0)), Variable(torch.from_numpy(oned_to_onehot(action)).unsqueeze(0)).type(dtype), Variable(torch.from_numpy(ct).unsqueeze(0)).type(dtype)), 1)).data.numpy()[0,0])
next_s = T(s, Action(action), R.t)
true_reward = R(s, Action(action), ct)
reward_sum += reward
true_reward_sum += true_reward
#next_state = running_state(next_state)
mask = 1
if t == args.max_ep_length - 1:
R.terminal = True
mask = 0
memory.push(s.state, np.array([oned_to_onehot(action)]), mask, next_s.state, reward, ct)
if args.render:
env.render()
if R.terminal:
break
s = next_s
num_steps += (t-1)
num_episodes += 1
reward_batch += reward_sum
true_reward_batch += true_reward_sum
reward_batch /= num_episodes
true_reward_batch /= num_episodes
gen_batch = memory.sample()
expert_batch = expert.sample(size=args.num_expert_trajs)
torch.from_numpy(ct).unsqueeze(0)).type(dtype)),
1)).data.cpu().numpy()[0, 0])
next_s = T(s, Action(action), R.t)
true_reward = R(s, Action(action), ct)
reward_sum += reward
true_reward_sum += true_reward
#next_state = running_state(next_state)
mask = 1
if t == args.max_ep_length - 1:
R.terminal = True
mask = 0
memory.push(s.state, np.array([oned_to_onehot(action)]), mask,
next_s.state, reward, ct)
if args.render:
env.render()
if R.terminal:
break
s = next_s
ep_memory.push(memory)
num_steps += (t - 1)
num_episodes += 1
reward_batch += reward_sum
true_reward_batch += true_reward_sum
def train(rank, args, shared_model, opt_ac, can_save, shared_obs_stats):
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 = [1] * 48
if args.render and can_save:
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())
ac_net.zero_grad()
num_steps = 0
reward_batch = 0
num_episodes = 0
#Tot_loss = 0
#Tot_num =
while num_steps < args.batch_size:
#state = env.reset()
#print(num_steps)
state = env.reset(difficulty=0)
last_state = process_observation(state)
state = process_observation(state)
last_state, state = transform_observation(last_state, state)
state = numpy.array(state)
#global last_state
#last_state,_ = update_observation(last_state,state)
#last_state,state = update_observation(last_state,state)
#print(state.shape[0])
#print(state[41])
state = Variable(torch.Tensor(state).unsqueeze(0))
shared_obs_stats.observes(state)
state = shared_obs_stats.normalize(state)
state = state.data[0].numpy()
#state = running_state(state)
reward_sum = 0
#timer = time.time()
for t in range(10000): # Don't infinite loop while learning
#print(t)
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(state)
print(action)
print('ERROR')
raise RuntimeError('action NaN problem')
#print(action)
#print("------------------------")
#timer = time.time()
BB = numpy.append(action, action)
#print(BB)
reward = 0
if args.skip:
#env.step(action)
_, A, _, _ = env.step(BB)
reward += A
_, A, _, _ = env.step(BB)
reward += A
next_state, A, done, _ = env.step(BB)
reward += A
next_state = process_observation(next_state)
last_state, next_state = transform_observation(
last_state, next_state)
next_state = numpy.array(next_state)
reward_sum += reward
#print('env:')
#print(time.time()-timer)
#last_state ,next_state = update_observation(last_state,next_state)
#next_state = running_state(next_state)
next_state = Variable(torch.Tensor(next_state).unsqueeze(0))
shared_obs_stats.observes(next_state)
next_state = shared_obs_stats.normalize(next_state)
next_state = next_state.data[0].numpy()
#print(next_state[41:82])
mask = 1
if done:
mask = 0
memory.push(state, np.array([action]), mask, next_state,
reward)
#if args.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()
#print('env:')
#print(time.time()-timer)
#timer = time.time()
update_params_actor_critic(batch, args, shared_model, ac_net, opt_ac)
#print('backpropagate:')
#print(time.time()-timer)
epoch = i_episode
if (i_episode % args.log_interval == 0) and (rank == 0):
print('TrainEpisode {}\tLast reward: {}\tAverage reward {:.2f}'.
format(i_episode, reward_sum, reward_batch))
if reward_batch > best_result:
best_result = reward_batch
save_model(
{
'epoch': epoch,
'bh': args.bh,
'state_dict': ac_net.state_dict(),
'optimizer': opt_ac,
'obs': shared_obs_stats,
}, PATH_TO_MODEL, 'best')
if epoch % 30 == 1:
save_model(
{
'epoch': epoch,
'bh': args.bh,
'state_dict': ac_net.state_dict(),
'optimizer': opt_ac,
'obs': shared_obs_stats,
}, 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 = []
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
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)
if reward != 0:
print("Reward: ", reward)
reward_sum += reward
next_state = torch.from_numpy(running_state(
board_image)).double().to(device).unsqueeze(0)
mask = 1
if t == horizon - 1:
mask = 0
memory.push(state.cpu().detach().numpy(),
np.array([action_array]), mask,
next_state.cpu().detach().numpy(), reward,
prev_x_pos, x_pos)
state = next_state
if saving_flag:
file_name = saving_folder + "trpo_horizon_" + str(
horizon) + "_iteration_" + str(
i_training_episode) + ".h5"
dataset = h5py.File(file_name, 'w')
board_image_array = np.concatenate(board_image_list,
axis=0)
board_image_array = np.transpose(board_image_array,
(0, 2, 1, 3))