# caclulate features
features, next_hidden_state_h, next_hidden_state_c = agent.feature_net(map_state, depth_state, goal_state, hidden_state_h, hidden_state_c)
# caclulate action and state values
dist, value, std = agent.ac_model( features)
total_std.append(std[1].cpu().numpy())
action = dist.sample()
# this is a x,1 tensor is kontains alle the possible actions
# the cpu command move it from a gpu tensor to a cpu tensor
next_map_state, next_depth_state, next_goal_state, reward, done, _ = envs.step(action.cpu().numpy())
# count reached goal and reset stacked frames
for i in range(0, num_envs):
if (done[i] == True):
number_of_episodes += 1
if (reward[i] >= 0.2):
number_reached_goal += 1
reach_goal.append(1)
else:
reach_goal.append(0)
_, stacked_map_frames = reset_single_frame(stacked_map_frames, next_map_state[i], stack_size, i)
_, stacked_depth_frames = reset_single_frame(stacked_depth_frames, next_depth_state[i], stack_size,
i)
_, stacked_goal_frames = reset_single_frame(stacked_goal_frames, next_goal_state[i], stack_size, i)
while not early_stop:
log_probs = []
values = []
states = []
actions = []
rewards = []
masks = []
for _ in range(NB_STEP):
state = torch.FloatTensor(state)
value = model.predict_value(state)
action = model.get_action(state)
action = action.squeeze(0)
next_state, reward, done, _ = envs.step(action)
log_prob = model.get_log_prob(state, action)
log_probs.append(log_prob)
values.append(value)
rewards.append(torch.FloatTensor(reward).unsqueeze(1))
masks.append(torch.FloatTensor(1 - done).unsqueeze(1))
states.append(state)
actions.append(action)
state = next_state
frame_idx += 1
if frame_idx % 1000 == 0:
test_reward = np.mean([test_env() for _ in range(10)])
def main():
mode = "regular"
num_envs = 16
def make_env():
def _thunk():
env = MiniPacman(mode, 1000)
return env
return _thunk
envs = [make_env() for i in range(num_envs)]
envs = SubprocVecEnv(envs)
state_shape = envs.observation_space.shape
#a2c hyperparams:
gamma = 0.99
entropy_coef = 0.01
value_loss_coef = 0.5
max_grad_norm = 0.5
num_steps = 5
num_frames = int(10e3)
#rmsprop hyperparams:
lr = 7e-4
eps = 1e-5
alpha = 0.99
#Init a2c and rmsprop
actor_critic = ActorCritic(envs.observation_space.shape,
envs.action_space.n)
optimizer = optim.RMSprop(actor_critic.parameters(),
lr,
eps=eps,
alpha=alpha)
#if USE_CUDA:
# actor_critic = actor_critic.cuda()
rollout = RolloutStorage(num_steps, num_envs, envs.observation_space.shape)
#rollout.cuda()
all_rewards = []
all_losses = []
state = envs.reset()
state = torch.FloatTensor(np.float32(state))
rollout.states[0].copy_(state)
episode_rewards = torch.zeros(num_envs, 1)
final_rewards = torch.zeros(num_envs, 1)
for i_update in tqdm(range(num_frames)):
for step in range(num_steps):
action = actor_critic.act(autograd.Variable(state))
next_state, reward, done, _ = envs.step(
action.squeeze(1).cpu().data.numpy())
reward = torch.FloatTensor(reward).unsqueeze(1)
episode_rewards += reward
masks = torch.FloatTensor(1 - np.array(done)).unsqueeze(1)
final_rewards *= masks
final_rewards += (1 - masks) * episode_rewards
episode_rewards *= masks
#if USE_CUDA:
# masks = masks.cuda()
state = torch.FloatTensor(np.float32(next_state))
rollout.insert(step, state, action.data, reward, masks)
_, next_value = actor_critic(
autograd.Variable(rollout.states[-1], volatile=True))
next_value = next_value.data
returns = rollout.compute_returns(next_value, gamma)
logit, action_log_probs, values, entropy = actor_critic.evaluate_actions(
autograd.Variable(rollout.states[:-1]).view(-1, *state_shape),
autograd.Variable(rollout.actions).view(-1, 1))
values = values.view(num_steps, num_envs, 1)
action_log_probs = action_log_probs.view(num_steps, num_envs, 1)
advantages = autograd.Variable(returns) - values
value_loss = advantages.pow(2).mean()
action_loss = -(autograd.Variable(advantages.data) *
action_log_probs).mean()
optimizer.zero_grad()
loss = value_loss * value_loss_coef + action_loss - entropy * entropy_coef
loss.backward()
nn.utils.clip_grad_norm(actor_critic.parameters(), max_grad_norm)
optimizer.step()
if i_update % num_frames == 0:
all_rewards.append(final_rewards.mean())
all_losses.append(loss.item())
#clear_output(True)
plt.figure(figsize=(20, 5))
plt.subplot(131)
plt.title('epoch %s. reward: %s' %
(i_update, np.mean(all_rewards[-10:])))
plt.plot(all_rewards)
plt.subplot(132)
plt.title('loss %s' % all_losses[-1])
plt.plot(all_losses)
plt.show()
rollout.after_update()
torch.save(actor_critic.state_dict(), "actor_critic_" + mode)
import time
def displayImage(image, step, reward):
#clear_output(True)
s = "step: " + str(step) + " reward: " + str(reward)
plt.figure(figsize=(10, 3))
plt.title(s)
plt.imshow(image)
plt.show()
time.sleep(0.1)
env = MiniPacman(mode, 1000)
done = False
state = env.reset()
total_reward = 0
step = 1
while not done:
current_state = torch.FloatTensor(state).unsqueeze(0)
#if USE_CUDA:
# current_state = current_state.cuda()
action = actor_critic.act(autograd.Variable(current_state))
next_state, reward, done, _ = env.step(action.data[0, 0])
total_reward += reward
state = next_state
image = torch.FloatTensor(state).permute(1, 2, 0).cpu().numpy()
displayImage(image, step, total_reward)
step += 1
if __name__ == '__main__':
envs = [make_env for i in range(N_ENVS)]
envs = SubprocVecEnv(envs)
obs = envs.reset()
print("OBSERVATION ", obs[0])
obs = obs.reshape(-1)
obs_shape = obs.shape
envs = VecNormalize(envs, obs_shape, ob=False, gamma=0.99)
n_steps = 100
bar = ProgBar(n_steps, bar_char='█')
for i_episode in range(2):
## reinitialize the environment
observation = envs.reset()
## the simulation for n_steps timesteps
for t in range(n_steps):
## value, is_rate, is_producer, is_open
actions_inje = [[randint(410, 430), False, False, True]
for _ in range(8)]
actions_prod = [[randint(220, 250), False, True, True]
for _ in range(4)]
## Advance the simulation forward
observation, reward, done, observation_full = \
envs.step([(actions_inje + actions_prod) for _ in range(N_ENVS)])
# print (reward)
bar.update()
if done.any():
print("Episode finished after {} timesteps".format(t + 1))
break
envs.close()
class RolloutCollector:
def __init__(self, num_env_workers, make_env_func, agent, batch_size,
rollout_length, num_recurrence_steps, state_shape,
action_shape, stats):
''' -one agent is assigned to a collector.
-a collector runs a bunch of envs in paralel to feed to that agent
-you could run a bunch of collectors simultaniously,
|- and then use weight mixing on the agents seperately
'''
self.num_env_workers = num_env_workers
self.envs = SubprocVecEnv(
[make_env_func() for i in range(num_env_workers)])
self.agent = agent
self.batch_size = batch_size
self.rollout_length = rollout_length
self.num_recurrence_steps = num_recurrence_steps
self.state_shape = state_shape
self.action_shape = action_shape
self.stats = stats
self.buffer_full = False
self.GAE_calculated = False
self.gamma = 0.8
self.tau = 0.8
self.rollout_indices = np.zeros(batch_size)
self.buffer_width = self.rollout_length + self.num_recurrence_steps - 1
self.states = torch.zeros(
(batch_size, self.buffer_width + 1, *state_shape),
dtype=torch.float32).to(self.agent.device)
self.actions = torch.zeros(
(batch_size, self.buffer_width + 1, *action_shape),
dtype=torch.float32).to(self.agent.device)
self.log_probs = torch.zeros(
(batch_size, self.buffer_width + 1, *action_shape),
dtype=torch.float32).to(self.agent.device)
self.values = torch.zeros((batch_size, self.buffer_width + 1, 1),
dtype=torch.float32).to(self.agent.device)
self.rewards = torch.zeros((batch_size, self.buffer_width + 1, 1),
dtype=torch.float32).to(self.agent.device)
self.done_masks = torch.zeros(
(batch_size, self.buffer_width + 1, 1),
dtype=torch.float32).to(self.agent.device)
self.advantages = torch.zeros(
(batch_size, self.buffer_width + 1, 1),
dtype=torch.float32).to(self.agent.device)
self.returns = torch.zeros((batch_size, self.buffer_width + 1, 1),
dtype=torch.float32).to(self.agent.device)
self.state = self.envs.reset()
self.hidden_state = torch.zeros(
(1, self.num_env_workers,
self.agent.hidden_state_size)).to(self.agent.device)
self.cell_state = torch.zeros(
(1, self.num_env_workers,
self.agent.hidden_state_size)).to(self.agent.device)
def collect_samples(self):
if self.buffer_full:
raise Exception(
"tried to collect more samples when buffer already full")
num_runs_to_full = math.ceil(self.batch_size / self.num_env_workers)
with torch.no_grad():
self.hidden_state = torch.zeros(
(1, self.num_env_workers,
self.agent.hidden_state_size)).to(self.agent.device)
self.cell_state = torch.zeros(
(1, self.num_env_workers,
self.agent.hidden_state_size)).to(self.agent.device)
for collection_run in range(num_runs_to_full):
start_index = collection_run * self.num_env_workers
end_index_exclusive = min(start_index + self.num_env_workers,
self.batch_size)
run_indices = torch.arange(start_index,
end_index_exclusive,
dtype=torch.long)
worker_indices = run_indices % self.num_env_workers
for rollout_idx in range(self.buffer_width + 1):
state = torch.Tensor(self.state).float().to(
self.agent.device)
# for recurrences
lstm_input = state.view(-1, 1, *self.state_shape)
output, (hidden, cell) = self.agent.lstm(
lstm_input, (self.hidden_state, self.cell_state))
output = output.reshape(self.num_env_workers,
self.agent.hidden_state_size)
policy_dist = self.agent.actor(output)
action = policy_dist.sample()
action = action.clamp(-1, 1) # depends on env
state_, reward, done, info = self.envs.step(
action.cpu().numpy())
value = self.agent.critic(output)
log_prob = policy_dist.log_prob(action)
reward = torch.Tensor(reward).float().unsqueeze(1).to(
self.agent.device)
done_masks = torch.Tensor(1.0 -
done).float().unsqueeze(1).to(
self.agent.device)
self.states[run_indices,
rollout_idx] = state[worker_indices]
self.actions[run_indices,
rollout_idx] = action[worker_indices]
self.log_probs[run_indices,
rollout_idx] = log_prob[worker_indices]
self.values[run_indices,
rollout_idx] = value[worker_indices]
self.rewards[run_indices,
rollout_idx] = reward[worker_indices]
self.done_masks[run_indices,
rollout_idx] = done_masks[worker_indices]
self.hidden_state[0, worker_indices] *= self.done_masks[
run_indices,
rollout_idx].expand(-1, self.agent.hidden_state_size)
self.cell_state[0, worker_indices] *= self.done_masks[
run_indices,
rollout_idx].expand(-1, self.agent.hidden_state_size)
self.state = state_
self.buffer_full = True
self.stats.update_collection_stats(
num_samples_collected_inc=self.batch_size * self.rollout_length)
def compute_gae(self):
if not self.buffer_full:
raise Exception(
"buffer is not full of new samples yet (so not ready for GAE)")
gae = torch.zeros((self.batch_size, 1)).to(self.agent.device)
for i in reversed(range(self.buffer_width)):
delta = self.rewards[:,
i] + self.gamma * self.values[:, i +
1] * self.done_masks[:,
i] - self.values[:,
i]
gae = delta + self.gamma * self.tau * self.done_masks[:, i] * gae
self.returns[:, i] = gae + self.values[:, i]
self.advantages[:, i] = gae
self.GAE_calculated = True
def get_leading_states(self, index):
indices_with_leading_states = torch.arange(
self.num_recurrence_steps) - self.num_recurrence_steps + 1 + index
leading_states = self.states[:, indices_with_leading_states]
# some of the leading states might be from previous episodes
# # in which case, we dont want to consider those at all.
leading_state_indices = indices_with_leading_states[:-1]
leading_dones = 1 - self.done_masks[:, leading_state_indices]
last_leading_dones = leading_dones.nonzero()[:, :2]
for batch_index, last_done in last_leading_dones:
previous_episode_indices = torch.arange(last_done + 1)
leading_states[batch_index, previous_episode_indices] = 0
return leading_states
def random_batch_iter(self):
if not self.buffer_full and not self.GAE_calculated:
raise Exception(
"buffer is not ready for sampling yet. (not full/no GAE)")
'''-theres no way all the workers are aligned, especially after an episode or so.
so we might just be able to use a vertical index'''
batch_indices = torch.randperm(self.rollout_length)
# recurrence stuff
if self.num_recurrence_steps > 0:
batch_indices = torch.randperm(
self.rollout_length) + self.num_recurrence_steps - 1
self.hidden_state = torch.zeros(
(1, self.batch_size,
self.agent.hidden_state_size)).to(self.agent.device)
self.cell_state = torch.zeros(
(1, self.batch_size,
self.agent.hidden_state_size)).to(self.agent.device)
for i in range(self.rollout_length):
index = batch_indices[i]
leading_states = self.get_leading_states(index)
output, (hidden, cell) = self.agent.lstm(
leading_states, (self.hidden_state, self.cell_state))
state = output[:, -1, :]
action = self.actions[:, index]
log_prob = self.log_probs[:, index]
advantage = self.advantages[:, index]
return_ = self.returns[:, index]
yield state, action, log_prob, advantage, return_
def reset(self):
self.buffer_full = False
self.GAE_calculated = False
print("=> loaded checkpoint '{}' (global_t {})"
.format(best_path, checkpoint['global_t']))
else:
global_t=0
print("=> no checkpoint found at '{}'".format(best_path))
count=0
running_corrects=0
running_corrects1 =0
writer=SummaryWriter()
for i_update in range(num_frames):
optimizer.zero_grad()
for step in range(num_steps):
action= actor_critic.act(Variable(pstate1),Variable(pstate2),Variable(pstate3),Variable(state),Variable(gstate),Variable(pre_action))
#print("act:",action)
pim1,pim2,pim3,next_state,g_state, reward, done,gt_action,gt_state, shortest,pre_action = envs.step(action.cpu().data.numpy())
for i in range(num_envs):
my_path[i][-1]+=1
if reward[i]>5:
episode_success[i][-1]+=1
if done[i]:
shortest_path[i].append(shortest[i])
episode_success[i].append(0)
my_path[i].append(1)
#=====================================
reward = torch.FloatTensor(reward).unsqueeze(1)
episode_rewards += reward
masks = torch.FloatTensor(1-np.array(done)).unsqueeze(1)
count+=(1-masks).sum()
#final_rewards *= masks
final_rewards += (1-masks) * episode_rewards
action_bound=None,
rollout_steps=ROLLOUT_STEPS,
memory_capacity=4096,
summary_writer=None,
mode=0)
states = envs.reset()
states = [utils.combine_env_states(*state) for state in states]
padded_states = np.array(
[utils.pad_data(state, MAX_NUM_NODES, [1]) for state in states])
for step in range(PPO_STEPS):
if (step + 1) % 10 == 0:
print('Step', step)
padded_actions = swarmnet_agent.act_batch(
[padded_states, padded_edge_types], masks)[0]
next_states, rewards, dones, infos = envs.step([
padded_action[-num_boid:, :]
for padded_action, num_boid in zip(padded_actions, env_num_boids)
])
padded_states = np.array([
utils.pad_data(utils.combine_env_states(*state), MAX_NUM_NODES,
[1]) for state in next_states
])
end_t = time.time()
print("Time spent", end_t - start_t)
class PPO(object):
"""Main PPO class"""
def __init__(self, args):
""""Constructor which allows the PPO class to initialize the attributes of the class"""
self.args = args
self.random_seed()
# Check if GPU is available via CUDA driver
self.use_cuda = torch.cuda.is_available()
self.device = torch.device("cuda" if self.use_cuda else "cpu")
# Initialize the actor critic class
self.actor_critic = ActorCritic(
self.args.nb_states, self.args.nb_actions,
self.args.hidden_layer_size).to(self.device)
# Define the optimizer used for the optimization of the surrogate loss
self.optimizer = self.args.optimizer(self.actor_critic.parameters(),
self.args.lr)
# For training multiple instances of the env are needed (Shoulder model)
self.envs = [self.make_env() for i in range(self.args.num_envs)]
self.envs = SubprocVecEnv(self.envs)
# To validate the intermediate learning process one test env is needed
self.env_test = self.args.env
self.env_test.seed(self.args.seed)
self.env_test.set_scaling(self.args.output_scaling)
# Lists for Tensorboard to visualize learning process during learning
self.test_rewards = []
self.loss = []
self.lr = []
self.actor_grad_weight = []
self.action_bang_bang = []
self.lr.append(self.args.lr)
# Dump bin files
if self.args.play is False:
self.output_path = "trained_models" + '/PPO_{}'.format(
datetime.now().strftime('%Y%b%d_%H%M%S')) + "/"
os.mkdir(self.output_path)
self.writer = SummaryWriter(self.output_path)
#self.delta = (self.args.lr-self.args.lr_end)/1e6
def train(self):
"""Main training function"""
frame_idx = 0
state = self.envs.reset()
mean_100_reward = -np.inf
self.info()
while frame_idx < self.args.max_frames:
log_probs = []
values = []
states = []
actions = []
rewards = []
masks = []
entropy = self.args.entropy
for _ in range(self.args.nb_steps):
state = torch.FloatTensor(state).to(self.device)
dist, value = self.actor_critic(state)
action = dist.sample()
# Make sure action is loaded to CPU (not GPU)
next_state, reward, done, _ = self.envs.step(
action.cpu().numpy())
log_prob = dist.log_prob(action)
entropy += dist.entropy().mean()
log_probs.append(log_prob)
values.append(value)
rewards.append(
torch.FloatTensor(reward).unsqueeze(1).to(self.device))
masks.append(
torch.FloatTensor(1 - done).unsqueeze(1).to(self.device))
states.append(state)
actions.append(action)
state = next_state
frame_idx += 1
#self.scheduler()
# Evaluate training process and write data to tensorboard
if frame_idx % 1000 == 0:
test_reward = np.mean(
[self.test_env(self.args.vis) for _ in range(10)])
self.test_rewards.append(test_reward)
if self.args.play is False:
print("Mean reward: ",
np.round(np.mean(self.test_rewards[-101:-1]), 0))
if mean_100_reward < np.round(
np.mean(self.test_rewards[-101:-1]), 0):
mean_100_reward = np.round(
np.mean(self.test_rewards[-101:-1]), 0)
self.save_network(mean_100_reward)
if len(self.test_rewards) >= 10:
self.writer.add_scalar(
'data/reward',
np.mean(self.test_rewards[-11:-1]),
frame_idx * self.args.num_envs)
self.writer.add_scalar(
'data/ppo_loss', np.mean(self.loss[-11:-1]),
frame_idx * self.args.num_envs)
self.writer.add_scalar(
'data/nb_actions_outside_range',
np.mean(self.action_bang_bang[-11:-1]),
frame_idx * self.args.num_envs)
# if test_reward > threshold_reward: early_stop = True
next_state = torch.FloatTensor(next_state).to(self.device)
_, next_value = self.actor_critic(next_state)
returns = self.calc_gae(next_value, rewards, masks, values,
self.args.gamma, self.args.tau)
# detach() to take it away from the graph i.e. this operations are ignored for gradient calculations
returns = torch.cat(returns).detach()
log_probs = torch.cat(log_probs).detach()
values = torch.cat(values).detach()
states = torch.cat(states)
actions = torch.cat(actions)
advantage = returns - values
self.ppo_update(self.args.ppo_epochs, self.args.mini_batch_size,
states, actions, log_probs, returns, advantage,
self.args.clip)
def make_env(self):
# Private trunk function for calling the SubprocVecEnv class
def _trunk():
env = self.args.env # in this simple case the class TestEnv() is called (see openAI for more envs)
env.seed(self.args.seed)
env.set_scaling(self.args.output_scaling)
return env
return _trunk
def test_env(self, vis=False):
state = self.env_test.reset()
if vis:
self.env_test.render()
done = False
total_reward = 0
action_bang_bang = 0
step = 0
while not done:
step += 1
state = torch.FloatTensor(state).unsqueeze(0).to(self.device)
dist, _ = self.actor_critic(state)
action = dist.sample().cpu().numpy()[0]
force = action * self.args.output_scaling
next_state, reward, done, _ = self.env_test.step(action)
if force > 0.5 or force < -0.5:
action_bang_bang += 1
state = next_state
if vis:
self.env_test.render()
total_reward += reward
self.action_bang_bang.append(action_bang_bang / step)
return total_reward
# Plain functions except that one can call them from an instance or the class
@staticmethod
def calc_gae(next_value, rewards, masks, values, gamma=0.99, tau=0.95):
values = values + [next_value]
gae = 0
returns = []
for step in reversed(range(len(rewards))):
delta = rewards[step] + gamma * values[
step + 1] * masks[step] - values[step]
gae = delta + gamma * tau * masks[step] * gae
returns.insert(0, gae + values[step])
return returns
@staticmethod
def ppo_iter(mini_batch_size, states, actions, log_probs, returns,
advantage):
batch_size = states.size(0)
for _ in range(batch_size // mini_batch_size):
rand_ids = np.random.randint(0, batch_size, mini_batch_size)
yield states[rand_ids, :], actions[rand_ids, :], log_probs[
rand_ids, :], returns[rand_ids, :], advantage[rand_ids, :]
def ppo_update(self,
ppo_epochs,
mini_batch_size,
states,
actions,
log_probs,
returns,
advantages,
clip_param=0.2):
for _ in range(ppo_epochs):
for state, action, old_log_probs, return_, advantage in self.ppo_iter(
mini_batch_size, states, actions, log_probs, returns,
advantages):
dist, value = self.actor_critic(state)
entropy = dist.entropy().mean()
new_log_probs = dist.log_prob(action)
ratio = (new_log_probs - old_log_probs).exp()
surr1 = ratio * advantage
surr2 = torch.clamp(ratio, 1.0 - clip_param,
1.0 + clip_param) * advantage
actor_loss = -torch.min(surr1, surr2).mean()
critic_loss = (return_ - value).pow(2).mean()
loss = 0.5 * critic_loss + actor_loss - 0.001 * entropy
self.loss.append(loss.item())
# Important step:
self.optimizer.zero_grad()
#pdb.set_trace()
loss.backward()
if self.args.grad_norm is not None:
nn.utils.clip_grad_norm_(self.actor_critic.parameters(),
self.args.grad_norm)
self.optimizer.step()
def save_network(self, reward):
network_path = self.output_path + "/network" + str(reward)
pickle.dump(self.actor_critic.state_dict(), open(network_path, "wb"))
def load_network(self, path):
network_new = pickle.load(open(path, "rb"))
self.actor_critic.load_state_dict(network_new)
def random_seed(self):
torch.manual_seed(self.args.seed)
random.seed(self.args.seed)
np.random.seed(self.args.seed)
def scheduler(self):
for g in self.optimizer.param_groups:
lr = g["lr"]
if self.args.lr_end > lr:
lr = self.args.lr_end
else:
lr -= self.delta
self.lr.append(lr)
g["lr"] = lr
def info(self):
fhandler = logging.FileHandler(filename=self.output_path +
'/mylog.log',
mode='a')
logger.addHandler(fhandler)
logger.info("--- INFO ---")
logger.info("args: {}".format(self.args))
sess.run(tf.global_variables_initializer())
while frame_idx < max_frames and not early_stop:
log_probs = []
values = []
obs = []
acs = []
rewards = []
masks = []
entropy = 0
for _ in range(num_steps):
ac = ppo.get_action(ob)
next_ob, reward, done, _ = envs.step(ac)
value = ppo.get_value(ob)
values.append(value)
rewards.append(reward[:, np.newaxis])
masks.append((1 - done)[:, np.newaxis])
obs.append(ob)
acs.append(ac)
ob = next_ob
frame_idx += 1
if frame_idx % 1000 == 0:
test_reward = np.mean([test_env(ppo) for _ in range(10)])
test_rewards.append(test_reward)
def main():
num_envs = 16
envs = [make_env() for i in range(num_envs)]
envs = SubprocVecEnv(envs)
env = gym.make("CartPole-v0")
num_inputs = envs.observation_space.shape[0]
num_outputs = envs.action_space.n
# Hyper params:
hidden_size = 256
lr = 3e-4
num_steps = 5
model = ActorCritic(num_inputs,num_outputs,hidden_size).to(device)
optimizer = optim.Adam(model.parameters())
max_frames = 20000
frame_idx = 0
test_rewards = []
state = envs.reset()
while frame_idx < max_frames:
log_probs = []
values = []
rewards = []
masks = []
entropy = 0
#每个子网络运行num_steps个steps,实现n步采样
for _ in range(num_steps):
state = torch.FloatTensor(state).to(device)
dist, value = model(state)
action = dist.sample()
next_state, reward, done, _ = envs.step(action.cpu().numpy())
log_prob = dist.log_prob(action)
entropy += dist.entropy().mean()
#记录下这num_steps步的各子网络相关参数
log_probs.append(log_prob)
values.append(value)
rewards.append(torch.FloatTensor(reward).unsqueeze(1).to(device))
masks.append(torch.FloatTensor(1 - done).unsqueeze(1).to(device))
state = next_state
frame_idx += 1
if frame_idx % 100 == 0:
test_rewards.append(np.mean([test_env(model, env) for _ in range(10)]))
plot(frame_idx, test_rewards)
#将子网络的参数传给主网络，并进行参数更新
next_state = torch.FloatTensor(next_state).to(device)
_, next_value = model(next_state)
returns = compute_returns(next_value, rewards, masks)
#将5个step的值串起来
log_probs = torch.cat(log_probs)
returns = torch.cat(returns).detach()
values = torch.cat(values)
advantage = returns - values
#计算loss均值
actor_loss = -(log_probs * advantage.detach()).mean()
critic_loss = advantage.pow(2).mean()
loss = actor_loss + 0.5 * critic_loss - 0.001 * entropy
optimizer.zero_grad()
loss.backward()
optimizer.step()
def train(env, agent, flags):
""""""
# set random seeds (for reproducibility)
torch.manual_seed(flags['seed'])
torch.cuda.manual_seed_all(flags['seed'])
envs = [make_env(flags['env'], flags['seed'], i) for i in range(flags['num_envs'])]
envs = SubprocVecEnv(envs)
# instantiate the policy and optimiser
num_inputs = envs.observation_space.shape[0]
num_outputs = envs.action_space.n
optimizer = optim.Adam(model.parameters(), lr=learning_rate)
current_step_number = 0
test_rewards = []
state = envs.reset()
while current_step_number < flags['max_steps']:
log_probs = []
values = []
rewards = []
masks = []
entropy = 0
for _ in range(flags['num_step_td_update']):
# sample an action from the distribution
action = agent.act(state)
# take a step in the environment
next_state, reward, done, _ = envs.step(action.cpu().numpy())
# compute the log probability
log_prob = dist.log_prob(action)
# compute the entropy
entropy += dist.entropy().mean()
# save the log probability, value and reward
log_probs.append(log_prob)
values.append(value)
rewards.append(torch.FloatTensor(reward).unsqueeze(1).to(device))
masks.append(torch.FloatTensor(1 - done).unsqueeze(1).to(device))
# if done, save episode rewards
state = next_state
current_step_number += 1
if current_step_number % 1000 and flags['plot_test'] == 0:
test_rewards.append(np.mean([test_env(model) for _ in range(10)]))
plot(current_step_number, test_rewards)
next_state = torch.FloatTensor(next_state).to(device)
_, next_value = model(next_state)
# calculate the discounted return of the episode
returns = compute_returns(next_value, rewards, masks)
log_probs = torch.cat(log_probs)
returns = torch.cat(returns).detach()
values = torch.cat(values)
advantage = returns - values
actor_loss = -(log_probs * advantage.detach()).mean()
critic_loss = advantage.pow(2).mean()
# loss function
loss = actor_loss + 0.5 * critic_loss - 0.001 * entropy
optimizer.zero_grad()
loss.backward()
optimizer.step()
return rewards
def dqn_algorithm(ENV_NAME,
NUM_ENV=8,
SEED=1,
TOTAL_TIMESTEPS=100000,
GAMMA=0.95,
MEMORY_SIZE=1000,
BATCH_SIZE=32,
EXPLORATION_MAX=1.0,
EXPLORATION_MIN=0.02,
EXPLORATION_FRACTION=0.7,
TRAINING_FREQUENCY=1000,
FILE_PATH='results/',
SAVE_MODEL=False,
MODEL_FILE_NAME='model',
LOG_FILE_NAME='log',
TIME_FILE_NAME='time',
PRINT_FREQ=100,
N_EP_AVG=100,
VERBOSE='False',
MLP_LAYERS=[64, 64],
MLP_ACTIVATIONS=['relu', 'relu'],
LEARNING_RATE=1e-3,
EPOCHS=1,
GRAD_CLIP=False,
DOUBLE_DQN=False,
USE_TARGET_NETWORK=True,
TARGET_UPDATE_FREQUENCY=5000,
LOAD_WEIGHTS=False,
LOAD_WEIGHTS_MODEL_PATH='results/model0.h5'):
'''
DQN Algorithm execution
env_name : string for a gym environment
num_env : no. for environment vectorization (multiprocessing env)
total_timesteps : Total number of timesteps
training_frequency : frequency of training (experience replay)
gamma : discount factor :
buffer_size : Replay buffer size
batch_size : batch size for experience replay
exploration_max : maximum exploration at the begining
exploration_min : minimum exploration at the end
exploration_fraction : fraction of total timesteps on which the exploration decay takes place
output_folder : output filepath
save_model : boolean to specify whether the model is to be saved
model_file_name : name of file to save the model at the end learning
log_file_name : name of file to store DQN results
time_file_name : name of file to store computation time
print_frequency : results printing episodic frequency
n_ep_avg : no. of episodes to be considered while computing average reward
verbose : print episodic results
mlp_layers : list of neurons in each hodden layer of the DQN network
mlp_activations : list of activation functions in each hodden layer of the DQN network
learning_rate : learning rate for the neural network
epochs : no. of epochs in every experience replay
grad_clip : boolean to specify whether to use gradient clipping in the optimizer (graclip value 10.0)
double_dqn : boolean to specify whether to employ double DQN
use_target_network : boolean to use target neural network in DQN
target_update_frequency : timesteps frequency to do weight update from online network to target network
load_weights : boolean to specify whether to use a prespecified model to initializa the weights of neural network
load_weights_model_path : path for the model to use for weight initialization
'''
before = time.time()
num_envs = NUM_ENV
env_name = ENV_NAME
if TOTAL_TIMESTEPS % NUM_ENV:
print('Error: total timesteps is not divisible by no. of envs')
return
def make_env():
def _thunk():
env = gym.make(env_name)
env.seed(SEED)
return env
return _thunk
envs = [make_env() for i in range(num_envs)]
envs = SubprocVecEnv(envs)
# for reproducibility
set_seed(SEED)
observation_space = envs.observation_space.shape[0]
action_space = envs.action_space.n
dqn_solver = DQNSolver(observation_space, action_space, MLP_LAYERS,
MLP_ACTIVATIONS, LEARNING_RATE, EPOCHS,
USE_TARGET_NETWORK, GRAD_CLIP, DOUBLE_DQN,
LOAD_WEIGHTS, LOAD_WEIGHTS_MODEL_PATH,
TOTAL_TIMESTEPS, MEMORY_SIZE, BATCH_SIZE, GAMMA,
EXPLORATION_MAX, EXPLORATION_MIN,
EXPLORATION_FRACTION)
envs = ParallelEnvWrapper(envs)
t = 0
episode_rewards = [0.0] * num_envs
explore_percent, episodes, mean100_rew, steps, NN_tr_loss = [],[],[],[],[]
while True:
state = envs.reset()
# state = np.reshape(state, [1, observation_space])
while True:
t += num_envs
dqn_solver.eps_timestep_decay(t)
action = dqn_solver.act(state)
state_next, reward, terminal, _ = envs.step(action)
# print(terminal)
# reward = reward if not terminal else -reward
# state_next = np.reshape(state_next, [1, observation_space])
dqn_solver.remember(state, action, reward, state_next, terminal)
if t % TRAINING_FREQUENCY == 0:
dqn_solver.experience_replay()
state = state_next
episode_rewards[-num_envs:] = [
i + j for (i, j) in zip(episode_rewards[-num_envs:], reward)
]
# num_episodes = len(episode_rewards)
# print(terminal)
if (t % PRINT_FREQ == 0):
explore_percent.append(dqn_solver.exploration_rate * 100)
episodes.append(len(episode_rewards))
mean100_rew.append(
round(np.mean(episode_rewards[(-1 - N_EP_AVG):-1]), 1))
steps.append(t)
NN_tr_loss.append(dqn_solver.loss)
if VERBOSE:
print('Exploration %: ' + str(int(explore_percent[-1])) +
' ,Episodes: ' + str(episodes[-1]) +
' ,Mean_reward: ' + str(mean100_rew[-1]) +
' ,timestep: ' + str(t) + ' , tr_loss: ' +
str(round(NN_tr_loss[-1], 4)))
if t > TOTAL_TIMESTEPS:
output_table = np.stack((steps, mean100_rew, episodes,
explore_percent, NN_tr_loss))
if not os.path.exists(FILE_PATH):
os.makedirs(FILE_PATH)
file_name = str(FILE_PATH) + LOG_FILE_NAME + '.csv'
np.savetxt(
file_name,
np.transpose(output_table),
delimiter=',',
header=
'Timestep,Rewards,Episodes,Exploration %,Training Score')
after = time.time()
time_taken = after - before
np.save(str(FILE_PATH) + TIME_FILE_NAME, time_taken)
if SAVE_MODEL:
file_name = str(FILE_PATH) + MODEL_FILE_NAME + '.h5'
dqn_solver.model.save(file_name)
return dqn_solver.model
if USE_TARGET_NETWORK and t % TARGET_UPDATE_FREQUENCY == 0:
dqn_solver.update_target_network()
# print(t)
if terminal.all():
episode_rewards += [0.0] * num_envs
break
n_updates = int(N_FRAMES // N_STEPS // N_ENVS)
for update_i in tqdm(range(n_updates)):
# Generate samples
for step in range(N_STEPS):
# Generate and take an action
with torch.no_grad():
value, action, action_log_prob = policy.act(
rollouts.observations[step])
take_actions = action.squeeze(1).cpu().numpy()
if len(take_actions.shape) == 1:
take_actions = np.expand_dims(take_actions, axis=-1)
obs, reward, done, info = envs.step(take_actions)
# convert to pytorch tensor
reward = torch.from_numpy(np.expand_dims(np.stack(reward), 1)).float()
masks = torch.FloatTensor([[0.0] if d else [1.0] for d in done])
# update reward info for logging
episode_rewards += reward
final_rewards *= masks
final_rewards += (1 - masks) * episode_rewards
episode_rewards *= masks
# Update our current observation tensor
current_obs *= masks
update_current_obs(obs)
while frame_idx < max_frames:
log_probs = []
values = []
states = []
actions = []
rewards = []
masks = []
entropy = 0
for _ in range(num_steps):
state = torch.FloatTensor(state).to(device)
dist, value = model(state)
action = dist.sample()
next_state, reward, done, _ = envs.step(
np.clip(action.cpu().numpy(), 0, 1))
log_prob = dist.log_prob(action)
entropy += dist.entropy().mean()
log_probs.append(log_prob)
values.append(value)
rewards.append(torch.FloatTensor(reward).unsqueeze(1).to(device))
masks.append(torch.FloatTensor(1 - done).unsqueeze(1).to(device))
states.append(state)
actions.append(action)
state = next_state
frame_idx += 1
early_stop = False
while frame_idx < max_frames and not early_stop:
i_update += 1
values = []
obs = []
acs = []
rewards = []
masks = []
entropy = 0
for _ in range(num_steps):
ac = ppo.get_action(ob)
next_ob, _, done, _ = envs.step(ac)
reward = discriminator.get_reward(np.concatenate([ob, ac], axis=1))
value = ppo.get_value(ob)
values.append(value)
rewards.append(reward[:, np.newaxis])
masks.append((1-done)[:, np.newaxis])
obs.append(ob)
acs.append(ac)
ob = next_ob
frame_idx += 1
if frame_idx % 1000 == 0:
test_reward = np.mean([test_env(ppo) for _ in range(10)])
while frame_idx < max_frames: log_probs = [] values = [] states = [] actions = [] rewards = [] masks = [] entropy = 0 for _ in range(num_steps): state = torch.FloatTensor(state).to(device) dist, value = model(state) action = dist.sample() next_state, reward, done, _ = envs.step(np.clip(action.cpu().numpy(), 0, 1)) log_prob = dist.log_prob(action) entropy += dist.entropy().mean() log_probs.append(log_prob) values.append(value) rewards.append(torch.FloatTensor(reward).unsqueeze(1).to(device)) masks.append(torch.FloatTensor(1 - done).unsqueeze(1).to(device)) states.append(state) actions.append(action) state = next_state frame_idx += 1
while not early_stop:
log_probs = []
values = []
states = []
actions = []
rewards = []
masks = []
for _ in tqdm(range(PPO_STEPS), ascii=True):
state = torch.FloatTensor(state).permute(0, 3, 1, 2).to(device)
dist, value = model(state)
action = dist.sample()
# each state, reward, done is a list of results from each parallel environment
next_state, reward, done, _ = envs.step(torch.argmax(action, dim=1, keepdim=True).cpu().numpy())
log_prob = dist.log_prob(action)
log_probs.append(log_prob)
values.append(value)
rewards.append(torch.FloatTensor(reward).unsqueeze(1).to(device))
masks.append(torch.FloatTensor(1 - done).unsqueeze(1).to(device))
states.append(state)
actions.append(action)
state = next_state
frame_idx += 1
next_state = torch.FloatTensor(next_state).permute(0, 3, 1, 2).to(device)
_, next_value = model(next_state)
def main():
envs = [make_env() for i in range(num_envs)]
envs = SubprocVecEnv(envs)
state_shape = envs.observation_space.shape
num_actions = envs.action_space.n
num_rewards = len(task_rewards[mode])
full_rollout = True
env_model = EnvModel(envs.observation_space.shape, num_pixels, num_rewards)
env_model.load_state_dict(torch.load("env_model_" + mode))
distil_policy = ActorCritic(envs.observation_space.shape,
envs.action_space.n)
distil_optimizer = optim.Adam(distil_policy.parameters())
imagination = ImaginationCore(1,
state_shape,
num_actions,
num_rewards,
env_model,
distil_policy,
full_rollout=full_rollout)
actor_critic = I2A(state_shape,
num_actions,
num_rewards,
256,
imagination,
full_rollout=full_rollout)
#rmsprop hyperparams:
lr = 7e-4
eps = 1e-5
alpha = 0.99
optimizer = optim.RMSprop(actor_critic.parameters(),
lr,
eps=eps,
alpha=alpha)
#if USE_CUDA:
# env_model = env_model.cuda()
# distil_policy = distil_policy.cuda()
# actor_critic = actor_critic.cuda()
gamma = 0.99
entropy_coef = 0.01
value_loss_coef = 0.5
max_grad_norm = 0.5
num_steps = 5
num_frames = int(10e5)
rollout = RolloutStorage(num_steps, num_envs, envs.observation_space.shape)
#rollout.cuda()
all_rewards = []
all_losses = []
state = envs.reset()
current_state = torch.FloatTensor(np.float32(state))
rollout.states[0].copy_(current_state)
episode_rewards = torch.zeros(num_envs, 1)
final_rewards = torch.zeros(num_envs, 1)
for i_update in tqdm(range(num_frames)):
for step in range(num_steps):
#if USE_CUDA:
# current_state = current_state.cuda()
action = actor_critic.act(autograd.Variable(current_state))
next_state, reward, done, _ = envs.step(
action.squeeze(1).cpu().data.numpy())
reward = torch.FloatTensor(reward).unsqueeze(1)
episode_rewards += reward
masks = torch.FloatTensor(1 - np.array(done)).unsqueeze(1)
final_rewards *= masks
final_rewards += (1 - masks) * episode_rewards
episode_rewards *= masks
#if USE_CUDA:
# masks = masks.cuda()
current_state = torch.FloatTensor(np.float32(next_state))
rollout.insert(step, current_state, action.data, reward, masks)
_, next_value = actor_critic(
autograd.Variable(rollout.states[-1], volatile=True))
next_value = next_value.data
returns = rollout.compute_returns(next_value, gamma)
logit, action_log_probs, values, entropy = actor_critic.evaluate_actions(
autograd.Variable(rollout.states[:-1]).view(-1, *state_shape),
autograd.Variable(rollout.actions).view(-1, 1))
distil_logit, _, _, _ = distil_policy.evaluate_actions(
autograd.Variable(rollout.states[:-1]).view(-1, *state_shape),
autograd.Variable(rollout.actions).view(-1, 1))
distil_loss = 0.01 * (F.softmax(logit).detach() *
F.log_softmax(distil_logit)).sum(1).mean()
values = values.view(num_steps, num_envs, 1)
action_log_probs = action_log_probs.view(num_steps, num_envs, 1)
advantages = autograd.Variable(returns) - values
value_loss = advantages.pow(2).mean()
action_loss = -(autograd.Variable(advantages.data) *
action_log_probs).mean()
optimizer.zero_grad()
loss = value_loss * value_loss_coef + action_loss - entropy * entropy_coef
loss.backward()
nn.utils.clip_grad_norm(actor_critic.parameters(), max_grad_norm)
optimizer.step()
distil_optimizer.zero_grad()
distil_loss.backward()
optimizer.step()
if i_update % 100 == 0:
all_rewards.append(final_rewards.mean())
all_losses.append(loss.item())
#clear_output(True)
plt.figure(figsize=(20, 5))
plt.subplot(131)
plt.title('epoch %s. reward: %s' %
(i_update, np.mean(all_rewards[-10:])))
plt.plot(all_rewards)
plt.subplot(132)
plt.title('loss %s' % all_losses[-1])
plt.plot(all_losses)
plt.show()
rollout.after_update()
torch.save(actor_critic.state_dict(), "i2a_" + mode)
log_probs = []
values = []
rewards = []
masks = []
entropy = 0
# rollout trajectory
for _ in range(num_steps):
state = torch.FloatTensor(state).to(device) # get a state from env
dist, value = model(
state
) # run the state through the network to get an action distribution and value of state
action = dist.sample(
) # pick an action from the action distribution output by the model
next_state, reward, done, _ = envs.step(action.cpu().numpy(
)) # take the action, and get a new state and reward
log_prob = dist.log_prob(action) # log prob of the action
entropy += dist.entropy().mean() # entropy
log_probs.append(log_prob) # add the log prob to a list
values.append(value) # add a list of predicted values
rewards.append(torch.FloatTensor(reward).unsqueeze(1).to(
device)) # add to a list of rewards
masks.append(torch.FloatTensor(1 - done).unsqueeze(1).to(device))
state = next_state
frame_idx += 1
if frame_idx % 100 == 0:
test_rewards.append(np.mean([test_env() for _ in range(10)]))
final_rewards = torch.zeros(num_envs, 1)
actor_critic.to(DEVICE)
rollout.to(DEVICE)
state = envs.reset()
state = torch.FloatTensor(np.float32(state)).to(DEVICE)
rollout.states[0].copy_(state)
for i_update in range(last_num_frames, num_frames):
for step in range(num_steps):
action = actor_critic.act(state)
next_state, reward, done, _ = envs.step(
action.squeeze(1).cpu().data.numpy())
reward = torch.FloatTensor(reward).unsqueeze(1)
episode_rewards += reward
masks = torch.FloatTensor(1 - np.array(done)).unsqueeze(1)
final_rewards *= masks
final_rewards += (1 - masks) * episode_rewards
episode_rewards *= masks
masks.to(DEVICE)
state = torch.FloatTensor(np.float32(next_state)).to(DEVICE)
rollout.insert(step, state, action.data, reward, masks)
with torch.no_grad():
_, next_value = actor_critic(rollout.states[-1])
class RolloutCollector:
def __init__(self, num_env_workers, make_env_func, agent, batch_size,
rollout_length, state_shape, action_shape, stats):
''' -one agent is assigned to a collector.
-a collector runs a bunch of envs in paralel to feed to that agent
-you could run a bunch of collectors simultaniously,
|- and then use weight mixing on the agents seperately
'''
#self.storage_device = torch.device("cpu")
self.num_env_workers = num_env_workers
self.envs = SubprocVecEnv(
[make_env_func() for i in range(num_env_workers)])
self.agent = agent
self.batch_size = batch_size
self.rollout_length = rollout_length
self.state_shape = state_shape
self.action_shape = action_shape
self.stats = stats
self.buffer_full = False
self.GAE_calculated = False
self.gamma = 0.8
self.tau = 0.8
self.rollout_indices = np.zeros(batch_size)
self.states = torch.zeros(
(batch_size, rollout_length + 1, *state_shape),
dtype=torch.float32).to(self.agent.device)
self.actions = torch.zeros(
(batch_size, rollout_length + 1, *action_shape),
dtype=torch.float32).to(self.agent.device)
self.log_probs = torch.zeros(
(batch_size, rollout_length + 1, *action_shape),
dtype=torch.float32).to(self.agent.device)
self.values = torch.zeros((batch_size, rollout_length + 1, 1),
dtype=torch.float32).to(self.agent.device)
self.rewards = torch.zeros((batch_size, rollout_length + 1, 1),
dtype=torch.float32).to(self.agent.device)
self.done_masks = torch.zeros(
(batch_size, rollout_length + 1, 1),
dtype=torch.float32).to(self.agent.device)
self.advantages = torch.zeros(
(batch_size, rollout_length + 1, 1),
dtype=torch.float32).to(self.agent.device)
self.returns = torch.zeros((batch_size, rollout_length + 1, 1),
dtype=torch.float32).to(self.agent.device)
self.state = self.envs.reset()
def collect_samples(self):
if self.buffer_full:
raise Exception(
"tried to collect more samples when buffer already full")
num_runs_to_full = math.ceil(self.batch_size / self.num_env_workers)
with torch.no_grad():
for collection_run in range(num_runs_to_full):
start_index = collection_run * self.num_env_workers
end_index_exclusive = min(start_index + self.num_env_workers,
self.batch_size)
run_indices = torch.arange(start_index,
end_index_exclusive,
dtype=torch.long)
worker_indices = run_indices % self.num_env_workers
for rollout_idx in range(self.rollout_length + 1):
state = torch.Tensor(self.state).float().to(
self.agent.device)
policy_dist = self.agent.actor(state)
action = policy_dist.sample()
if self.agent.tanh_action_clamping:
action = torch.tanh(action)
else:
action = action.clamp(-1, 1) # depends on env
cpu_actions = action.cpu().numpy()
state_, reward, done, info = self.envs.step(cpu_actions)
value = self.agent.critic(state)
log_prob = policy_dist.log_prob(action)
reward = torch.Tensor(reward).float().unsqueeze(1).to(
self.agent.device)
done_masks = torch.Tensor(1.0 -
done).float().unsqueeze(1).to(
self.agent.device)
self.states[run_indices,
rollout_idx] = state[worker_indices]
self.actions[run_indices,
rollout_idx] = action[worker_indices]
self.log_probs[run_indices,
rollout_idx] = log_prob[worker_indices]
self.values[run_indices,
rollout_idx] = value[worker_indices]
self.rewards[run_indices,
rollout_idx] = reward[worker_indices]
self.done_masks[run_indices,
rollout_idx] = done_masks[worker_indices]
self.state = state_
self.buffer_full = True
self.stats.update_collection_stats(
num_samples_collected_inc=self.batch_size * self.rollout_length)
def compute_gae(self):
if not self.buffer_full:
raise Exception(
"buffer is not full of new samples yet (so not ready for GAE)")
gae = torch.zeros((self.batch_size, 1)).to(self.agent.device)
for i in reversed(range(self.rollout_length)):
delta = self.rewards[:,
i] + self.gamma * self.values[:, i +
1] * self.done_masks[:,
i] - self.values[:,
i]
gae = delta + self.gamma * self.tau * self.done_masks[:, i] * gae
self.returns[:, i] = gae + self.values[:, i]
self.advantages[:, i] = gae
self.GAE_calculated = True
def random_batch_iter(self):
if not self.buffer_full and not self.GAE_calculated:
raise Exception(
"buffer is not ready for sampling yet. (not full/no GAE)")
'''-theres no way all the workers are aligned, especially after an episode or so.
so we might just be able to use a vertical index'''
batch_indices = torch.randperm(self.rollout_length)
for i in range(self.rollout_length):
index = batch_indices[i]
state = self.states[:, index]
action = self.actions[:, index]
log_prob = self.log_probs[:, index]
advantage = self.advantages[:, index]
return_ = self.returns[:, index]
yield state, action, log_prob, advantage, return_
def reset(self):
self.buffer_full = False
self.GAE_calculated = False
while not completed:
# reset data for new epoch, i.e. on ploicy training
log_probs = []
values = []
states = []
actions = []
rewards = []
masks = []
for i in range(num_steps_for_batch):
state = torch.tensor(state, dtype=torch.float32, device=device)
normal_dist, critic_value = model.forward(state)
action = normal_dist.sample()
next_state, reward, done, _ = envs.step(action.detach().numpy())
states.append(state)
actions.append(action)
rewards.append(reward)
masks.append((1 - done))
log_probs.append(normal_dist.log_prob(action))
values.append(critic_value)
state = next_state
total_steps += 1
# validation every 4000 steps
if total_steps % 4000 == 0:
test_reward, max_distance = test_model(number_of_test_runs)
class env_cover():
def __init__(self, config, dev):
self.dev = dev
self.num_env = config['num_envs']
self.get_img_from_render = config['get_img_from_render']
self.obs_shape = (self.num_env, ) + config['obs_space'][1:]
# print(self.obs_shape)
self.reward_shape = (self.num_env, ) + config['reward_space'][1:]
self.gamma_shape = (self.num_env, ) + config['gamma_space'][1:]
if self.num_env == 1:
self.env = gym.make(config['game_name'])
else:
def make_env():
def _thunk():
env = gym.make(config['game_name'])
return env
return _thunk
envs = [make_env() for i in range(self.num_env)]
self.env = SubprocVecEnv(envs)
#
#def obs_preproc(x):
# if IMG_GET_RENDER ==False:
# return torch.from_numpy(np.resize(x, feature_state)).float().unsqueeze(0)
# x = np.dot(x, np.array([[0.299, 0.587, 0.114]]).T)
# x = np.reshape(x, (1,x.shape[1], x.shape[0]))
# return torch.from_numpy(np.resize(x, feature_state)).float().unsqueeze(0)/255
#
def reset(self):
st = self.env.reset()
if self.get_img_from_render:
st = self.env.render(mode='rgb_array')
st = np.resize(st, self.obs_shape) / 255.
return torch.FloatTensor(st).reshape(self.obs_shape).to(
self.dev), torch.zeros(self.reward_shape).to(
self.dev), torch.zeros(self.gamma_shape).to(self.dev)
#return st, 0,False
# def get_obs(self,obs):
# return torch.from_numpy(obs).detach().float().view(1,config['obs_space'])
def step(self, action):
st, rt, dt, _ = self.env.step(action)
if self.get_img_from_render:
st = self.env.render(mode='rgb_array')
st = np.resize(st, self.obs_shape) / 255.
# print(st)
st = torch.FloatTensor(st).reshape(self.obs_shape).to(self.dev)
rt = torch.FloatTensor([rt]).reshape(self.reward_shape).to(self.dev)
if self.num_env == 1:
dt = torch.FloatTensor([dt]).reshape(self.gamma_shape).to(self.dev)
else:
dt = torch.FloatTensor(dt.astype(int)).reshape(
self.gamma_shape).to(self.dev)
return st, rt, dt
def end_dummy(self):
return torch.zeros(self.obs_shape).to(self.dev), torch.zeros(
self.reward_shape).to(self.dev), torch.zeros(self.gamma_shape).to(
self.dev)
def render(self):
self.env.render()
def close(self):
self.env.close()
while frame_idx < max_frames:
log_probs = []
values = []
rewards = []
masks = []
entropy = 0
# rollout trajectory
for _ in range(num_steps):
state = torch.FloatTensor(state).to(device)
dist, value = model(state)
action = dist.sample()
next_state, reward, done, _ = envs.step(action.cpu().numpy())
log_prob = dist.log_prob(action)
entropy += dist.entropy().mean()
log_probs.append(log_prob)
values.append(value)
rewards.append(torch.FloatTensor(reward).unsqueeze(1).to(device))
masks.append(torch.FloatTensor(1 - done).unsqueeze(1).to(device))
state = next_state
frame_idx += 1
if frame_idx % 100 == 0:
test_rewards.append(np.mean([test_env() for _ in range(10)]))
plot(frame_idx, test_rewards)
def train(args):
# hyper-params:
frame_idx = 0
hidden_size = args.hidden_size
lr = args.lr
num_steps = args.num_steps
mini_batch_size = args.mini_batch_size
ppo_epochs = args.ppo_epochs
threshold_reward = args.threshold_reward
max_frames = args.max_frames
# test_rewards = []
num_envs = args.num_envs
test_epochs = args.test_epochs
resume_training = args.resume_training
best_test_reward = 0.0
urdf_path = os.path.join(BASE_DIR, os.pardir, "snake/snake.urdf")
log_dir = args.log_dir
now = datetime.now()
log_dir = log_dir + '_' + now.strftime('%d_%m_%Y_%H_%M_%S')
# Check cuda availability.
use_cuda = torch.cuda.is_available()
device = torch.device("cuda" if use_cuda else "cpu")
p.connect(p.DIRECT)
writer = SummaryWriter(log_dir)
# Create training log.
textio = utils.IOStream(os.path.join(log_dir, 'train.log'), args=args)
# textio.log_params(device, num_envs, lr, threshold_reward)
utils.logFiles(log_dir)
# create multiple environments.
envs = [utils.make_env(p, urdf_path, args=args) for i in range(num_envs)]
envs = SubprocVecEnv(envs)
# pdb.set_trace() # Debug
num_inputs = envs.observation_space.shape[0]
num_outputs = envs.action_space.shape[0]
# Create Policy/Network
net = ActorCritic(num_inputs, num_outputs, hidden_size).to(device)
optimizer = optim.Adam(net.parameters(), lr=lr)
# If use pretrained policy.
if resume_training:
if os.path.exists(resume_training):
checkpoint = torch.load(resume_training)
frame_idx = checkpoint['frame_idx']
net.load_state_dict(checkpoint['model'])
best_test_reward = checkpoint['best_test_reward']
# Initial Reset for Environment.
state = envs.reset()
early_stop = False
# Create env for policy testing.
robot = snake.Snake(p, urdf_path, args=args)
env = SnakeGymEnv(robot, args=args)
print_('\nTraining Begins ...', color='r', style='bold')
textio.log('Training Begins ...')
while frame_idx < max_frames and not early_stop:
print_('\nTraining Policy!', color='r', style='bold')
textio.log('\n############## Epoch: %0.5d ##############'%(int(frame_idx/20)))
# Memory buffers
log_probs = []
values = []
states = []
actions = []
rewards = []
masks = []
entropy = 0
total_reward = 0.0
for i in range(num_steps):
print('Steps taken: {} & Epoch: {}\r'.format(i, int(frame_idx/20)), end="")
state = torch.FloatTensor(state).to(device)
# Find action using policy.
dist, value = net(state)
action = dist.sample()
action = action #HACK
# Take actions and find MDP.
next_state, reward, done, _ = envs.step(action.cpu().numpy())
total_reward += sum(reward)
textio.log('Steps: {} and Reward: {}'.format(int(frame_idx%20), total_reward))
# Calculate log(policy)
log_prob = dist.log_prob(action)
entropy += dist.entropy().mean()
# Create Experiences
log_probs.append(log_prob)
values.append(value)
rewards.append(torch.FloatTensor(reward).unsqueeze(1).to(device))
masks.append(torch.FloatTensor(1 - done).unsqueeze(1).to(device))
states.append(state)
actions.append(action)
# Update state.
state = next_state
frame_idx += 1
# Test Trained Policy.
if frame_idx % 40 == 0:
print_('\n\nEvaluate Policy!', color='bl', style='bold')
test_reward = np.mean([utils.test_env(env, net, test_idx) for test_idx in range(test_epochs)])
# test_rewards.append(test_reward)
# utils.plot(frame_idx, test_rewards) # not required due to tensorboardX.
writer.add_scalar('test_reward', test_reward, frame_idx)
print_('\nTest Reward: {}\n'.format(test_reward), color='bl', style='bold')
textio.log('Test Reward: {}'.format(test_reward))
# Save various factors of training.
snap = {'frame_idx': frame_idx,
'model': net.state_dict(),
'best_test_reward': best_test_reward,
'optimizer' : optimizer.state_dict()}
if best_test_reward < test_reward:
save_checkpoint(snap, os.path.join(log_dir, 'weights_bestPolicy.pth'))
best_test_reward = test_reward
save_checkpoint(snap, os.path.join(log_dir,'weights.pth'))
if test_reward > threshold_reward: early_stop = True
if frame_idx % 1000 == 0:
if not os.path.exists(os.path.join(log_dir, 'models')): os.mkdir(os.path.join(log_dir, 'models'))
save_checkpoint(snap, os.path.join(log_dir, 'models', 'weights_%0.5d.pth'%frame_idx))
# Calculate Returns
next_state = torch.FloatTensor(next_state).to(device)
_, next_value = net(next_state)
returns = compute_gae(next_value, rewards, masks, values)
# Concatenate experiences for multiple environments.
returns = torch.cat(returns).detach()
log_probs = torch.cat(log_probs).detach()
values = torch.cat(values).detach()
states = torch.cat(states)
actions = torch.cat(actions)
advantage = returns - values
writer.add_scalar('reward/episode', total_reward, frame_idx)
textio.log('Total Training Reward: {}'.format(total_reward))
# Update the Policy.
ppo_update(net, optimizer, ppo_epochs, mini_batch_size, states, actions, log_probs, returns, advantage, writer, frame_idx)
