def collect_samples(pid, queue, env, policy, stochastic, render, running_state, min_batch_size):
torch.randn(pid)
log = dict()
memory = Memory()
num_steps = 0
total_reward = 0
min_reward = 1e6
max_reward = -1e6
num_episodes = 0
while num_steps < min_batch_size:
state = env.reset()
if running_state is not None:
state = running_state(state)
reward_episode = 0
for t in range(10000):
state_var = tensor(state).unsqueeze(0)
with torch.no_grad():
if not stochastic:
action = policy(state_var)[0][0].numpy()
else:
action = policy.select_action(state_var)[0].numpy()
action = action.astype(np.float64)
next_state, reward, done, _ = env.step(action)
reward_episode += reward
if running_state is not None:
next_state = running_state(next_state)
mask = 0 if done else 1
memory.push(state, action, mask, next_state, reward)
if render:
env.render()
if done:
break
state = next_state
# log stats
num_steps += (t + 1)
num_episodes += 1
total_reward += reward_episode
min_reward = min(min_reward, reward_episode)
max_reward = max(max_reward, reward_episode)
log['num_steps'] = num_steps
log['num_episodes'] = num_episodes
log['total_reward'] = total_reward
log['avg_reward'] = total_reward / num_episodes
log['max_reward'] = max_reward
log['min_reward'] = min_reward
if queue is not None:
queue.put([pid, memory, log])
else:
return memory, log
def collect_samples(pid, queue, env, policy, custom_reward, mean_action,
render, running_state, min_batch_size):
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
total_reward = 0
min_reward = 1e6
max_reward = -1e6
total_c_reward = 0
min_c_reward = 1e6
max_c_reward = -1e6
num_episodes = 0
while num_steps < min_batch_size:
state = env.reset()
if running_state is not None:
state = running_state(state)
reward_episode = 0
for t in range(10000):
state_var = tensor(state).unsqueeze(0)
with torch.no_grad():
if mean_action:
action = policy(state_var)[0][0].numpy()
else:
action = policy.select_action(state_var)[0].numpy()
action = int(action) if policy.is_disc_action else action.astype(
np.float64)
next_state, reward, done, _ = env.step(action)
reward_episode += reward
if running_state is not None:
next_state = running_state(next_state)
if custom_reward is not None:
reward = custom_reward(state, action)
total_c_reward += reward
min_c_reward = min(min_c_reward, reward)
max_c_reward = max(max_c_reward, reward)
mask = 0 if done else 1
memory.push(state, action, mask, next_state, reward)
if render:
env.render()
if done:
break
state = next_state
# log stats
num_steps += (t + 1)
num_episodes += 1
total_reward += reward_episode
min_reward = min(min_reward, reward_episode)
max_reward = max(max_reward, reward_episode)
log['num_steps'] = num_steps
log['num_episodes'] = num_episodes
log['total_reward'] = total_reward
log['avg_reward'] = total_reward / num_episodes
log['max_reward'] = max_reward
log['min_reward'] = min_reward
if custom_reward is not None:
log['total_c_reward'] = total_c_reward
log['avg_c_reward'] = total_c_reward / num_steps
log['max_c_reward'] = max_c_reward
log['min_c_reward'] = min_c_reward
if queue is not None:
queue.put([pid, memory, log])
else:
return memory, log
def train_gail(self, expert):
'''Train Info-GAIL.'''
args, dtype = self.args, self.dtype
results = {
'average_reward': [],
'episode_reward': [],
'true_traj': {},
'pred_traj': {}
}
self.train_step_count, self.gail_step_count = 0, 0
for ep_idx in range(args.num_epochs):
memory = Memory()
num_steps = 0
reward_batch, true_reward_batch = [], []
expert_true_reward_batch = []
true_traj_curr_episode, gen_traj_curr_episode = [], []
while num_steps < args.batch_size:
traj_expert = expert.sample(size=1)
state_expert, action_expert, _, _ = traj_expert
# Expert state and actions
state_expert = state_expert[0]
action_expert = action_expert[0]
expert_episode_len = len(state_expert)
# Sample start state or should we just choose the start state
# from the expert trajectory sampled above.
# curr_state_obj = self.sample_start_state()
curr_state_obj = State(state_expert[0], self.obstacles)
curr_state_feat = self.get_state_features(
curr_state_obj, self.args.use_state_features)
# Add history to state
if args.history_size > 1:
curr_state = -1 * np.ones(
(args.history_size * curr_state_feat.shape[0]),
dtype=np.float32)
curr_state[(args.history_size-1) \
* curr_state_feat.shape[0]:] = curr_state_feat
else:
curr_state = curr_state_feat
# TODO: Make this a separate function. Can be parallelized.
ep_reward, ep_true_reward, expert_true_reward = 0, 0, 0
true_traj, gen_traj = [], []
gen_traj_dict = {
'features': [],
'actions': [],
'c': [],
'mask': []
}
disc_reward, posterior_reward = 0.0, 0.0
# Use a hard-coded list for memory to gather experience since we
# need to mutate it before finally creating a memory object.
c_sampled = np.zeros((self.num_goals), dtype=np.float32)
c_sampled[np.random.randint(0, self.num_goals)] = 1.0
c_sampled_tensor = torch.zeros((1)).type(torch.LongTensor)
c_sampled_tensor[0] = int(np.argmax(c_sampled))
if self.args.cuda:
c_sampled_tensor = torch.cuda.LongTensor(c_sampled_tensor)
memory_list = []
for t in range(expert_episode_len):
action = self.select_action(
np.concatenate((curr_state, c_sampled)))
action_numpy = action.data.cpu().numpy()
# Save generated and true trajectories
true_traj.append((state_expert[t], action_expert[t]))
gen_traj.append((curr_state_obj.coordinates, action_numpy))
gen_traj_dict['features'].append(
self.get_state_features(curr_state_obj,
self.args.use_state_features))
gen_traj_dict['actions'].append(action_numpy)
gen_traj_dict['c'].append(c_sampled)
action = epsilon_greedy_linear_decay(action_numpy,
args.num_epochs * 0.5,
ep_idx,
self.action_size,
low=0.05,
high=0.3)
# Get the discriminator reward
disc_reward_t = float(
self.reward_net(
torch.cat((Variable(
torch.from_numpy(curr_state).unsqueeze(
0)).type(dtype),
Variable(
torch.from_numpy(
oned_to_onehot(
action, self.action_size)).
unsqueeze(0)).type(dtype)),
1)).data.cpu().numpy()[0, 0])
if args.use_log_rewards and disc_reward_t < 1e-6:
disc_reward_t += 1e-6
disc_reward_t = -math.log(disc_reward_t) \
if args.use_log_rewards else -disc_reward_t
disc_reward += disc_reward_t
# Predict c given (x_t)
predicted_posterior = self.posterior_net(
Variable(torch.from_numpy(curr_state).unsqueeze(
0)).type(dtype))
posterior_reward_t = self.criterion_posterior(
predicted_posterior,
Variable(c_sampled_tensor)).data.cpu().numpy()[0]
posterior_reward += (self.args.lambda_posterior *
posterior_reward_t)
# Update Rewards
ep_reward += (disc_reward_t + posterior_reward_t)
true_goal_state = [
int(x) for x in state_expert[-1].tolist()
]
if self.args.flag_true_reward == 'grid_reward':
ep_true_reward += self.true_reward.reward_at_location(
curr_state_obj.coordinates,
goals=[true_goal_state])
expert_true_reward += self.true_reward.reward_at_location(
state_expert[t], goals=[true_goal_state])
elif self.args.flag_true_reward == 'action_reward':
ep_true_reward += self.true_reward.reward_at_location(
np.argmax(action_expert[t]), action)
expert_true_reward += self.true_reward.corret_action_reward
else:
raise ValueError("Incorrect true reward type")
# Update next state
next_state_obj = self.transition_func(
curr_state_obj, Action(action), 0)
next_state_feat = self.get_state_features(
next_state_obj, self.args.use_state_features)
#next_state = running_state(next_state)
mask = 0 if t == expert_episode_len - 1 else 1
# Push to memory
memory_list.append([
curr_state,
np.array([oned_to_onehot(action,
self.action_size)]), mask,
next_state_feat, disc_reward_t + posterior_reward_t,
c_sampled, c_sampled
])
if args.render:
env.render()
if not mask:
break
curr_state_obj = next_state_obj
curr_state_feat = next_state_feat
if args.history_size > 1:
curr_state[:(args.history_size-1) \
* curr_state_feat.shape[0]] = \
curr_state[curr_state_feat.shape[0]:]
curr_state[(args.history_size-1) \
* curr_state_feat.shape[0]:] = curr_state_feat
else:
curr_state = curr_state_feat
assert memory_list[-1][2] == 0, \
"Mask for final end state is not 0."
for memory_t in memory_list:
memory.push(*memory_t)
self.logger.summary_writer.add_scalars(
'gen_traj/gen_reward', {
'discriminator': disc_reward,
'posterior': posterior_reward,
}, self.train_step_count)
num_steps += (t - 1)
reward_batch.append(ep_reward)
true_reward_batch.append(ep_true_reward)
expert_true_reward_batch.append(expert_true_reward)
results['episode_reward'].append(ep_reward)
# Append trajectories
true_traj_curr_episode.append(true_traj)
gen_traj_curr_episode.append(gen_traj)
results['average_reward'].append(np.mean(reward_batch))
# Add to tensorboard
self.logger.summary_writer.add_scalars(
'gen_traj/reward', {
'average': np.mean(reward_batch),
'max': np.max(reward_batch),
'min': np.min(reward_batch)
}, self.train_step_count)
self.logger.summary_writer.add_scalars(
'gen_traj/true_reward', {
'average': np.mean(true_reward_batch),
'max': np.max(true_reward_batch),
'min': np.min(true_reward_batch),
'expert_true': np.mean(expert_true_reward_batch)
}, self.train_step_count)
# Add predicted and generated trajectories to results
if ep_idx % self.args.save_interval == 0:
results['true_traj'][ep_idx] = copy.deepcopy(
true_traj_curr_episode)
results['pred_traj'][ep_idx] = copy.deepcopy(
gen_traj_curr_episode)
# Update parameters
gen_batch = memory.sample()
# We do not get the context variable from expert trajectories.
# Hence we need to fill it in later.
expert_batch = expert.sample(size=args.num_expert_trajs)
self.update_params(gen_batch, expert_batch, ep_idx,
args.optim_epochs, args.optim_batch_size)
self.train_step_count += 1
if ep_idx > 0 and ep_idx % args.log_interval == 0:
print('Episode [{}/{}] Avg R: {:.2f} Max R: {:.2f} \t' \
'True Avg {:.2f} True Max R: {:.2f} ' \
'Expert (Avg): {:.2f}'.format(
ep_idx, args.num_epochs, np.mean(reward_batch),
np.max(reward_batch), np.mean(true_reward_batch),
np.max(true_reward_batch),
np.mean(expert_true_reward_batch)))
results_path = os.path.join(args.results_dir, 'results.pkl')
with open(results_path, 'wb') as results_f:
pickle.dump((results), results_f, protocol=2)
# print("Did save results to {}".format(results_path))
if ep_idx % args.save_interval == 0:
checkpoint_filepath = self.model_checkpoint_filepath(ep_idx)
torch.save(self.checkpoint_data_to_save(), checkpoint_filepath)
print("Did save checkpoint: {}".format(checkpoint_filepath))
def collect_samples(pid, queue, env, policy, custom_reward, mean_action,
render, running_state, min_batch_size):
torch.randn(pid)
log = dict()
memory = Memory()
num_steps = 0
total_reward = 0
min_reward = 1e6
max_reward = -1e6
total_c_reward = 0
min_c_reward = 1e6
max_c_reward = -1e6
num_episodes = 0
while num_steps < min_batch_size:
state = env.reset()
if running_state is not None:
state = running_state(state)
reward_episode = 0
repeat = 0
repeat_len = 0
last_action = None
ready_to_push = False
reward_period = 0
interval = 0
for t in range(10000):
state_var = tensor(state).unsqueeze(0)
# Learning to Repeate only predicts when repeat reduced to 0.
assert (repeat >= 0)
if repeat <= 0:
with torch.no_grad():
if mean_action:
action = policy(state_var)[0][0].numpy()
else:
# action, repeat = policy.select_action(state_var)[0].numpy()
action, repeat = policy.select_action(state_var)
action = action[0].numpy()
repeat = repeat[0].numpy()
# print(action)
# print(repeat)
# exit()
action = int(
action) if policy.is_disc_action else action.astype(
np.float64)
# action = action.tolist()
last_action = action
repeat = int(repeat)
repeat_len = repeat
ready_to_push = True
reward_period = 0
interval = 0
next_state, reward, done, _ = env.step(last_action)
reward_episode += reward
# reward_period += reward
reward_period += reward * (args.gamma**(repeat_len - repeat))
interval += 1
if repeat > 0:
repeat -= 1
if running_state is not None:
next_state = running_state(next_state)
if custom_reward is not None:
reward = custom_reward(state, action)
total_c_reward += reward
min_c_reward = min(min_c_reward, reward)
max_c_reward = max(max_c_reward, reward)
mask = 0 if done else 1
if ready_to_push == True or done:
repeat = 0 if done else repeat
memory.push(state, last_action, mask, next_state,
reward_period, repeat)
ready_to_push = False
interval = 0
if render:
env.render()
if done:
# print(reward_episode)
# print(t)
break
state = next_state
# log stats
num_steps += (t + 1)
num_episodes += 1
total_reward += reward_episode
min_reward = min(min_reward, reward_episode)
max_reward = max(max_reward, reward_episode)
log['num_steps'] = num_steps
log['num_episodes'] = num_episodes
log['total_reward'] = total_reward
log['avg_reward'] = total_reward / num_episodes
log['max_reward'] = max_reward
log['min_reward'] = min_reward
if custom_reward is not None:
log['total_c_reward'] = total_c_reward
log['avg_c_reward'] = total_c_reward / num_steps
log['max_c_reward'] = max_c_reward
log['min_c_reward'] = min_c_reward
if queue is not None:
queue.put([pid, memory, log])
else:
return memory, log
def collect_samples(pid, queue, env, policy, custom_reward, mean_action,
tensor, render, running_state, update_rs, min_batch_size):
torch.randn(pid, )
log = dict()
memory = Memory()
num_steps = 0
total_reward = 0
min_reward = 1e6
max_reward = -1e6
total_c_reward = 0
min_c_reward = 1e6
max_c_reward = -1e6
num_episodes = 0
while num_steps < min_batch_size:
state = env.reset()
if running_state is not None:
state = running_state(state, update=update_rs)
reward_episode = 0
for t in range(10000):
state_var = Variable(tensor(state).unsqueeze(0), volatile=True)
if mean_action:
action = policy(state_var)[0].data[0].numpy()
else:
action = policy.select_action(state_var)[0].numpy()
action = int(action) if policy.is_disc_action else action.astype(np.float64)
next_state, reward, done, _ = env.step(action)
reward_episode += reward
if running_state is not None:
next_state = running_state(next_state, update=update_rs)
if custom_reward is not None:
reward = custom_reward(state, action)
total_c_reward += reward
min_c_reward = min(min_c_reward, reward)
max_c_reward = max(max_c_reward, reward)
mask = 0 if done else 1
memory.push(state, action, mask, next_state, reward)
if render:
env.render()
if done:
break
state = next_state
# log stats
num_steps += (t + 1)
num_episodes += 1
total_reward += reward_episode
min_reward = min(min_reward, reward_episode)
max_reward = max(max_reward, reward_episode)
log['num_steps'] = num_steps
log['num_episodes'] = num_episodes
log['total_reward'] = total_reward
log['avg_reward'] = total_reward / num_episodes
log['max_reward'] = max_reward
log['min_reward'] = min_reward
if custom_reward is not None:
log['total_c_reward'] = total_c_reward
log['avg_c_reward'] = total_c_reward / num_steps
log['max_c_reward'] = max_c_reward
log['min_c_reward'] = min_c_reward
if queue is not None:
queue.put([pid, memory, log])
else:
return memory, log
def collect_samples(pid, queue, env, policy, custom_reward,
mean_action, render, min_batch_size):
try:
if isinstance(env, str):
if render:
env = gym.make(env, render_mode='human')
else:
env = gym.make(env)
torch.randn(pid)
log = dict()
memory = Memory()
num_steps = 0
total_reward = 0
min_reward = 1e6
max_reward = -1e6
total_c_reward = 0
min_c_reward = 1e6
max_c_reward = -1e6
num_episodes = 0
while num_steps < min_batch_size:
state = env.reset()
reward_episode = 0
for t in range(600):
state_var = tensor(state).unsqueeze(0)
with torch.no_grad():
if mean_action:
action = policy(state_var)[0][0].numpy()
else:
action = policy.select_action(state_var)[0].numpy()
action = int(action) if policy.is_disc_action else action.astype(np.float64)
next_state, reward, done, _ = env.step(action)
reward_episode += reward
if custom_reward is not None:
reward = custom_reward(state, action)
total_c_reward += reward
min_c_reward = min(min_c_reward, reward)
max_c_reward = max(max_c_reward, reward)
mask = 0 if done else 1
memory.push(state, action, mask, next_state, reward)
if render:
env.render()
if done:
break
state = next_state
# log stats
num_steps += (t + 1)
num_episodes += 1
total_reward += reward_episode
min_reward = min(min_reward, reward_episode)
max_reward = max(max_reward, reward_episode)
log['num_steps'] = num_steps
log['num_episodes'] = num_episodes
log['total_reward'] = total_reward
log['avg_reward'] = total_reward / num_episodes
log['max_reward'] = max_reward
log['min_reward'] = min_reward
if custom_reward is not None:
log['total_c_reward'] = total_c_reward
log['avg_c_reward'] = total_c_reward / num_steps
log['max_c_reward'] = max_c_reward
log['min_c_reward'] = min_c_reward
if queue is not None:
queue.put([pid, memory, log])
else:
return memory, log
except Exception as e:
if queue is not None:
queue.put([pid, memory, log])
else:
raise e
def collect_samples(pid,
queue,
env,
policy,
custom_reward,
mean_action,
render,
running_state,
min_batch_size,
num_agents=1):
torch.randn(pid)
log = dict()
memory = Memory()
num_steps = 0
total_reward = [0] * num_agents #0
min_reward = 1e6
max_reward = -1e6
total_c_reward = 0
min_c_reward = 1e6
max_c_reward = -1e6
num_episodes = 0
# render=False
while num_steps < min_batch_size:
state = env.reset()
# print(state)
if running_state is not None:
state = running_state(state)
reward_episode = [0] * num_agents
# env.render()
for t in range(150):
# time.sleep(1)
#TODO: temporarily for a single agent we make the state
# artificially into a list.
# state_var = [tensor(state).unsqueeze(0), tensor(state).unsqueeze(0)]
state_var = [tensor(s).unsqueeze(0) for s in state]
# print('STATE', len(state_var))
# state_var = state_var[:-1]
# print(len(state_var))
# state_var = [tensor(state).unsqueeze(0)]
# print(state_var)
# state_var = tensor(state).unsqueeze(0)
with torch.no_grad():
if mean_action:
# print('mean', policy(state_var))
action = policy(state_var)[0][0].numpy()
else:
# print('else', policy.select_action(state_var))
# print(state_var)
action = policy.select_action(state_var)
# print(action[0][0].numpy())
# print(action)
# action_var = torch.stack([a[0] for a inaction, dim=1)
# action_var = torch.cat(action, dim=1)[0].numpy()
# action = [a[0] for a in action]
action = [a[0].numpy().tolist() for a in action]
# print(action_var)
# print(action)
# action = policy.select_action(state_var)[0].numpy()
# TODO: this is added so that the prey is automatically controlled
# by arbitrary input.
# action.append(np.array([0.0, 0.0, 0.0, 0.0, 0.0]))
# print('ACT', action)
# print(action)
action_var = action
# print(action_var)
# print(action)
# action = int(action) if policy.is_disc_action else action.astype(np.float64)
# print(action)
# action_var = action
# action_var = [int(a) for a in action_var] if policy.is_disc_action else [a.astype(np.float64) for a in action_var]
# print(action_var)
# print('aa', action)
# print('av', action_var)
# next_state, reward, done, _ = env.step(action)
# TODO: while we use an environment that doesn't accept multi-agent
# action lists
next_state, reward, done, _ = env.step(action_var)
# print(reward)
# reward = reward[:-1]
# done = done[:-1]
# reward_all = np.sum(reward)
# for r in range(len(reward)):
# reward[r] = reward_all
# print(reward)
# reward = [reward, reward]
# reward_episode += reward
for r in range(len(reward_episode)):
reward_episode[r] += reward[r]
# print(reward_episode)
if running_state is not None:
next_state = running_state(next_state)
if custom_reward is not None:
reward = custom_reward(state, action)
total_c_reward += reward
min_c_reward = min(min_c_reward, reward)
max_c_reward = max(max_c_reward, reward)
# mask = 0 if done else 1
mask = [float(d) for d in done]
# print(mask)
#TODO while we use an environment that doesn't accept multi-agent
# action lists
# print(state)
# state = [s.tolist() for s in state]
# print(state)
memory.push(state, action, mask, next_state, reward)
# memory.push(state, action, mask, next_state, reward)
if render:
env.render()
time.sleep(0.1)
if np.all(done):
break
state = next_state
# log stats
num_steps += (t + 1)
num_episodes += 1
for r in range(len(reward_episode)):
total_reward[r] += reward_episode[r]
# total_reward += reward_episode
min_reward = min(min_reward, np.min(reward_episode))
max_reward = max(max_reward, np.max(reward_episode))
# min_reward = 0.0
# max_reward = 0.0
log['num_steps'] = num_steps
log['avg_steps'] = num_steps / num_episodes
log['num_episodes'] = num_episodes
log['total_reward'] = total_reward
log['avg_reward'] = [t / num_episodes
for t in total_reward] #total_reward / num_episodes
log['max_reward'] = max_reward
log['min_reward'] = min_reward
if custom_reward is not None:
log['total_c_reward'] = total_c_reward
log['avg_c_reward'] = total_c_reward / num_steps
log['max_c_reward'] = max_c_reward
log['min_c_reward'] = min_c_reward
if queue is not None:
queue.put([pid, memory, log])
else:
return memory, log
def collect_samples(pid, queue, env, policy, custom_reward,
mean_action, render, running_state, min_batch_size, use_reparametrization=False):
torch.randn(pid)
log = dict()
memory = Memory()
num_steps = 0
total_reward = 0
min_reward = 1e6
max_reward = -1e6
total_c_reward = 0
min_c_reward = 1e6
max_c_reward = -1e6
num_episodes = 0
lenght_lists=[]
while num_steps < min_batch_size:
state = env.reset()
if running_state is not None:
state = running_state(state)
reward_episode = 0
for t in range(250):
state_var = tensor(state).unsqueeze(0)
with torch.no_grad():
if mean_action:
# action = policy(state_var)[0][0].numpy()
action = policy.select_action_deterministic.numpy()
else:
action = policy.select_action_stochastic(state_var)[0].numpy()
action = int(action) if policy.is_disc_action else action.astype(np.float64)
next_state, reward, done, _ = env.step(np.clip(action*100,a_min=-100, a_max=100))
reward_episode += reward
if running_state is not None:
next_state = running_state(next_state)
if custom_reward is not None:
reward = custom_reward(state, action)
total_c_reward += reward
min_c_reward = min(min_c_reward, reward)
max_c_reward = max(max_c_reward, reward)
mask = 0 if done else 1
memory.push(state, action, mask, next_state, reward)
if render:
env.render()
# / if (done) or ((t%249 ==0) and t>0):
if (done):
break
state = next_state
lenght_lists.append(t)
# log stats
num_steps += (t + 1)
num_episodes += 1
total_reward += reward_episode
min_reward = min(min_reward, reward_episode)
max_reward = max(max_reward, reward_episode)
log['num_steps'] = num_steps
log['num_episodes'] = num_episodes
log['total_reward'] = total_reward
log['avg_reward'] = total_reward / num_episodes
log['max_reward'] = max_reward
log['min_reward'] = min_reward
log['lenght_mean'] = np.mean(lenght_lists)
log['lenght_min'] = np.min(lenght_lists)
log['lenght_max'] = np.max(lenght_lists)
log['lenght_std'] = np.std(lenght_lists)
if custom_reward is not None:
log['total_c_reward'] = total_c_reward
log['avg_c_reward'] = total_c_reward / num_steps
log['avg_c_reward_per_episode'] = total_c_reward / num_episodes
log['max_c_reward'] = max_c_reward
log['min_c_reward'] = min_c_reward
if queue is not None:
queue.put([pid, memory, log])
else:
return memory, log
def collect_samples(pid, queue, env, policy, custom_reward, mean_action, tensor,
render, running_state, update_rs, min_batch_size, mode_list, state_type,
num_steps_per_mode):
torch.randn(pid, )
log = dict()
memory = Memory()
num_steps = 0
total_reward = 0
min_reward = 1e6
max_reward = -1e6
total_c_reward = 0
min_c_reward = 1e6
max_c_reward = -1e6
num_episodes = 0
max_t = num_steps_per_mode * len(mode_list) - 1
while num_steps < min_batch_size:
state = env.reset()
if state_type == 'decayed_context':
state = np.concatenate((state, np.array([1.0]),
activity_map(mode_list[0]),
activity_map(mode_list[min(1, len(mode_list)-1)])), axis=0)
elif state_type == 'context':
state = np.concatenate((state, activity_map(mode_list[0]),
activity_map(mode_list[min(1, len(mode_list)-1)])), axis=0)
if running_state is not None:
state = running_state(state, update=update_rs)
reward_episode = 0
for t in range(10000):
curr_mode_id = t // num_steps_per_mode
if t % num_steps_per_mode == 0:
if hasattr(env.env, 'mode'):
env.env.mode = mode_list[curr_mode_id]
state_var = Variable(tensor(state).unsqueeze(0), volatile=True)
if mean_action:
action = policy(state_var)[0].data[0].numpy()
else:
action = policy.select_action(state_var)[0].numpy()
action = int(action) if policy.is_disc_action else action.astype(np.float64)
next_state, reward, done, _ = env.step(action)
reward_episode += reward
next_mode_id = min(t+1, max_t) // num_steps_per_mode
if state_type == 'decayed_context':
next_state = np.concatenate((next_state,
np.array([1/((t % num_steps_per_mode) + 1)]),
activity_map(mode_list[next_mode_id]),
activity_map(mode_list[min(next_mode_id+1, len(mode_list)-1)])), axis=0)
elif state_type == 'context':
next_state = np.concatenate((next_state,
activity_map(mode_list[next_mode_id]),
activity_map(mode_list[min(next_mode_id+1, len(mode_list)-1)])), axis=0)
if running_state is not None:
next_state = running_state(next_state, update=update_rs)
if custom_reward is not None:
reward = custom_reward(state, action)
total_c_reward += reward
min_c_reward = min(min_c_reward, reward)
max_c_reward = max(max_c_reward, reward)
if t == num_steps_per_mode * len(mode_list) - 1:
done = True
mask = 0 if done else 1
memory.push(state, action, mask, next_state, reward)
if render:
env.render()
if done:
break
state = next_state
# log stats
num_steps += (t + 1)
num_episodes += 1
total_reward += reward_episode
min_reward = min(min_reward, reward_episode)
max_reward = max(max_reward, reward_episode)
log['num_steps'] = num_steps
log['num_episodes'] = num_episodes
log['total_reward'] = total_reward
log['avg_reward'] = total_reward / num_episodes
log['max_reward'] = max_reward
log['min_reward'] = min_reward
if custom_reward is not None:
log['total_c_reward'] = total_c_reward
log['avg_c_reward'] = total_c_reward / num_steps
log['max_c_reward'] = max_c_reward
log['min_c_reward'] = min_c_reward
if queue is not None:
queue.put([pid, memory, log])
else:
return memory, log
def collect_samples(pid, queue, env, policy, custom_reward, mean_action,
render, running_state, min_batch_size):
torch.randn(pid)
log = dict()
log['reward_list'] = list()
memory = Memory()
num_steps = 0
total_reward = 0
min_reward = 1e6
max_reward = -1e6
total_c_reward = 0
min_c_reward = 1e6
max_c_reward = -1e6
num_episodes = 0
# tbd, should match build main dtype
dtype = torch.float64
while num_steps < min_batch_size:
state = env.reset()
if running_state is not None:
state = running_state(state)
reward_episode = 0
for t in range(10000):
#print('t:{:.1f}\tnum_steps:{:.1f}\tmin_batch_size:{:.1f}'.format(t,num_steps,min_batch_size))
# tbd, add .to(dtype)
state_var = tensor(state).to(dtype).unsqueeze(0)
with torch.no_grad():
if mean_action:
action = policy(state_var)[0][0].numpy()
else:
action = policy.select_action(state_var)[0].numpy()
action = int(action) if policy.is_disc_action else action.astype(
np.float64)
next_state, reward, done, _ = env.step(action)
reward_episode += reward
if running_state is not None:
next_state = running_state(next_state)
if custom_reward is not None:
reward = custom_reward.expert_reward(state, action)
total_c_reward += reward
min_c_reward = min(min_c_reward, reward)
max_c_reward = max(max_c_reward, reward)
mask = 0 if done else 1
memory.push(state, action, mask, next_state, reward)
if render:
env.render()
if done:
break
#if t > min_batch_size:
# break
state = next_state
# log stats
num_steps += (t + 1)
num_episodes += 1
total_reward += reward_episode
min_reward = min(min_reward, reward_episode)
max_reward = max(max_reward, reward_episode)
log['reward_list'].append(reward_episode)
log['num_steps'] = num_steps
log['num_episodes'] = num_episodes
log['total_reward'] = total_reward
log['avg_reward'] = total_reward / num_episodes # tbd: num_episodes -> num_steps
log['max_reward'] = max_reward
log['min_reward'] = min_reward
if custom_reward is not None:
log['total_c_reward'] = total_c_reward
log['avg_c_reward'] = total_c_reward / num_steps
log['max_c_reward'] = max_c_reward
log['min_c_reward'] = min_c_reward
if queue is not None:
queue.put([pid, memory, log])
else:
return memory, log
def collect_samples(pid,
queue,
env,
policy,
custom_reward,
mean_action,
render,
running_state,
min_batch_size,
aux_running_state,
intervention_device=None):
torch.randn(pid)
log = dict()
extra_mem_fields = []
num_steps = 0
total_reward = 0
min_reward = 1e6
max_reward = -1e6
total_c_reward = 0
min_c_reward = 1e6
max_c_reward = -1e6
num_episodes = 0
aux_state = None
next_aux_state = None
car_racing_env = env.spec.id == 'CarRacing-v0'
is_img_state = len(env.observation_space.shape) == 3
if car_racing_env:
extra_mem_fields.extend(['aux_state', 'aux_next_state'])
if is_img_state:
img_t = img_transform(imgnet_means, imgnet_stds)
if intervention_device is not None:
intervener = Intervener(intervention_device, env.spec.id)
extra_mem_fields.append('expert_mask')
memory = Memory(extra_mem_fields)
while num_steps < min_batch_size:
state = env.reset()
if car_racing_env:
aux_state = np.array([np.linalg.norm(env.car.hull.linearVelocity)])
if running_state is not None:
state = running_state(state)
if aux_state is not None and aux_running_state is not None:
aux_state = aux_running_state(aux_state)
reward_episode = 0
for t in range(10000):
if is_img_state:
state_var = img_t(state).unsqueeze(0)
else:
state_var = tensor(state).unsqueeze(0)
if aux_state is not None:
aux_state_var = tensor(aux_state).view(1, -1).to(dtype)
with torch.no_grad():
if mean_action:
if aux_state is not None:
action = policy(state_var, aux_state_var)[0][0].numpy()
else:
action = policy(state_var)[0][0].numpy()
else:
if aux_state is not None:
action = policy.select_action(
state_var, aux_state_var)[0].numpy()
else:
action = policy.select_action(state_var)[0].numpy()
if intervention_device is not None:
intervene_action = intervener.get_action()
if np.any(intervene_action):
action = intervene_action
expert_action = 1
time.sleep(intervener.step_delay)
else:
expert_action = 0
# time.sleep(intervener.step_delay)
action = int(action) if policy.is_disc_action else action.astype(
np.float64)
next_state, reward, done, _ = env.step(action)
if car_racing_env:
next_aux_state = np.array(
[np.linalg.norm(env.car.hull.linearVelocity)])
reward_episode += reward
if running_state is not None:
next_state = running_state(next_state)
if next_aux_state is not None and aux_running_state is not None:
next_aux_state = aux_running_state(next_aux_state)
if custom_reward is not None:
if is_img_state:
reward = custom_reward(state, action, aux_state)
else:
reward = custom_reward(state, action)
total_c_reward += reward
min_c_reward = min(min_c_reward, reward)
max_c_reward = max(max_c_reward, reward)
# TODO remove this, temporary for faster testing
if t > 100:
done = True
mask = 0 if done else 1
if is_img_state:
mem_state = state_var.squeeze().numpy()
mem_next_state = img_t(next_state).numpy()
else:
mem_state = state
mem_next_state = next_state
mem_list = [mem_state, action, mask, mem_next_state, reward]
if aux_state is not None:
mem_list.extend([aux_state, next_aux_state])
if intervention_device is not None:
mem_list.append(expert_action)
memory.push(*mem_list)
if render:
env.render()
if done:
break
state = next_state
if aux_state is not None:
aux_state = next_aux_state
# log stats
num_steps += (t + 1)
num_episodes += 1
total_reward += reward_episode
min_reward = min(min_reward, reward_episode)
max_reward = max(max_reward, reward_episode)
log['num_steps'] = num_steps
log['num_episodes'] = num_episodes
log['total_reward'] = total_reward
log['avg_reward'] = total_reward / num_episodes
log['max_reward'] = max_reward
log['min_reward'] = min_reward
if custom_reward is not None:
log['total_c_reward'] = total_c_reward
log['avg_c_reward'] = total_c_reward / num_episodes
log['max_c_reward'] = max_c_reward
log['min_c_reward'] = min_c_reward
if queue is not None:
queue.put([pid, memory, log])
else:
return memory, log
def collect_samples(pid, queue, env, policy_mgr, policy_wrk, custom_reward,
mean_action, render, running_state, min_batch_size):
torch.randn(pid)
log = dict()
memory_mgr = Memory()
memory_wrk = Memory()
num_steps = 0
total_reward = 0
min_reward = 1e6
max_reward = -1e6
total_c_reward = 0
min_c_reward = 1e6
max_c_reward = -1e6
num_episodes = 0
avg_wrk_reward = 0
avg_mgr_reward = 0
mgr_steps = 0
done_count = 0
state, curr_pos = env.reset()
while num_steps < min_batch_size:
if running_state is not None:
state = running_state(state)
reward_episode = 0
# Manager
state_mgr = tensor(state).unsqueeze(0)
with torch.no_grad():
direction = policy_mgr.select_action(state_mgr)[0]
direction = int(direction.detach().numpy())
subgoal = get_target(curr_pos, direction)
# Worker
state_wrk = tensor(np.concatenate((state, subgoal)))
for t in range(10000):
# Sample Action
with torch.no_grad():
if mean_action:
action = policy(state_var)[0][0].numpy()
else:
action = policy_wrk.select_action(
state_wrk.unsqueeze(0))[0].numpy()
# Take Action
next_state, reward, done, info = env.step(action)
## Sparse Rewards
dist = np.linalg.norm(info['fingertip'] - info['target'])
reward = -1 if (dist > 0.05) else 0
next_state_wrk = np.concatenate((next_state, subgoal))
reward_episode += reward
if running_state is not None:
next_state = running_state(next_state)
if custom_reward is not None:
reward = custom_reward(state, action)
total_c_reward += reward
min_c_reward = min(min_c_reward, reward)
max_c_reward = max(max_c_reward, reward)
mask_mgr = 0 if done else 1
# Intrinsic Reward and Subgoal Reached Definition
reward_wrk = -np.linalg.norm(
subgoal - info['fingertip']) + info['reward_ctrl']
subgoal_reached = (-reward_wrk < 0.05)
mask_wrk = 0 if (done or subgoal_reached) else 1
# Collect Rollout
memory_wrk.push(state_wrk.detach().numpy(), action, mask_wrk,
next_state_wrk, reward_wrk)
avg_wrk_reward += reward_wrk
if render:
env.render()
if (done or subgoal_reached):
break
state_wrk = tensor(next_state_wrk)
# Manager Rollout
next_state_mgr = next_state
reward_mgr = reward_episode / 50.0
memory_mgr.push(state, direction, mask_mgr, next_state_mgr, reward_mgr)
state = next_state
avg_mgr_reward += reward_mgr
mgr_steps += 1
# log stats
num_steps += (t + 1)
if (done):
num_episodes += 1
min_reward = min(min_reward, reward_episode)
max_reward = max(max_reward, reward_episode)
state, curr_pos = env.reset()
total_reward += reward_episode
else:
curr_pos = info['fingertip']
log['num_steps'] = num_steps
log['mgr_steps'] = mgr_steps
log['num_episodes'] = num_episodes
log['total_reward'] = total_reward
log['avg_reward'] = total_reward / (num_episodes)
log['max_reward'] = max_reward
log['min_reward'] = min_reward
log['mgr_reward'] = avg_mgr_reward / mgr_steps
log['wrk_reward'] = avg_wrk_reward / num_steps
if custom_reward is not None:
log['total_c_reward'] = total_c_reward
log['avg_c_reward'] = total_c_reward / num_steps
log['max_c_reward'] = max_c_reward
log['min_c_reward'] = min_c_reward
if queue is not None:
queue.put([pid, memory, log])
else:
return memory_mgr, memory_wrk, log
def collect_samples(pid, obs_shape_n, act_shape_n, queue, env, policy,
custom_reward, mean_action, tensor, render, running_state,
update_rs, min_batch_size, g_itr):
n_agents = len(policy)
torch.randn(pid, )
log = dict()
memory = Memory()
num_steps = 0
total_reward = 0
min_reward = 1e6
max_reward = -1e6
total_c_reward = 0
min_c_reward = 1e6
max_c_reward = -1e6
num_episodes = 0
# EPS_MAX = 0.9995
# eps_val = EPS_MAX**float(g_itr)
# if eps_val < 0.1:
# eps_val = 0.1
# while num_steps < min_batch_size:
while num_steps < min_batch_size:
state = env.reset()
# print(state)
# if running_state is not None:
# state = running_state(state, update=update_rs)
reward_episode = 0
for t in range(10000):
num_steps += 1
action = []
rewards = []
state_var = Variable(tensor(state).unsqueeze(0), volatile=True)
if mean_action:
# never arrived
action = policy(state_var)[0].data[0].numpy()
else:
for i in range(n_agents):
# action = policy[i].select_ma_action(state_var, n_agents)[0].numpy()
action.append(policy[i].select_action(
state_var[:, i, :])[0].numpy()[0])
# freeze
# action[0] = 0
# action[1] = 0
# action[2] = 0
# action[0] = 0
# eps = np.random.randn(action.size)*eps_val
# action = action + eps
# np.clip(action, -1., 1.)
# print(action)
# action = int(action) if policy.is_disc_action else action.astype(np.float64)
one_hot_actions = []
for i in range(n_agents):
one_hot_actions.append(
index_to_one_hot(action[i], act_shape_n[i]))
# print(one_hot_actions)
next_state, reward, done, _ = env.step(one_hot_actions)
# Added for shaped reward by haiyinpiao.
# for punishing the bipedwalker from stucking in where it is originally.
# if (next_state[2]<0.2):
# reward -=2
# -------------------------
# print(reward)
reward_episode += np.mean(reward[3])
# if running_state is not None:
# next_state = running_state(next_state, update=update_rs)
# if custom_reward is not None:
# reward = custom_reward(state, action)
# total_c_reward += reward
# min_c_reward = min(min_c_reward, reward)
# max_c_reward = max(max_c_reward, reward)
# mask = 0 if done[0] else 1
mask = done
memory.push(state, action, mask, next_state, reward)
if render:
env.render()
# time.sleep(0.1)
# done[3] indicates if the good agents caught
if done[3] or num_steps >= min_batch_size:
break
# if done[0]:
# break
state = next_state
# log stats
# num_steps += (t + 1)
num_episodes += 1
total_reward += reward_episode
min_reward = min(min_reward, reward_episode)
max_reward = max(max_reward, reward_episode)
# print(pid,"collected!")
log['num_steps'] = num_steps
log['num_episodes'] = num_episodes
log['total_reward'] = total_reward
log['avg_reward'] = total_reward / num_episodes
log['max_reward'] = max_reward
log['min_reward'] = min_reward
if custom_reward is not None:
log['total_c_reward'] = total_c_reward
log['avg_c_reward'] = total_c_reward / num_steps
log['max_c_reward'] = max_c_reward
log['min_c_reward'] = min_c_reward
if queue is not None:
queue.put([pid, memory, log])
else:
return memory, log
def collect_samples(pid,
queue,
env,
policy,
custom_reward,
mean_action,
tensor,
render,
running_state,
update_rs,
min_batch_size,
logger,
position_vector,
log_flag=False):
torch.randn(pid, )
log = dict()
memory = Memory()
num_steps = 0
total_reward = 0
min_reward = 1e6
max_reward = -1e6
total_c_reward = 0
min_c_reward = 1e6
max_c_reward = -1e6
num_episodes = 0
while num_steps < min_batch_size:
obs = env.reset()
#TODO 设置全局变量 position vector
#CPG_controller = CPG_network(position_vector)
CPG_controller = CPG_network(position_vector)
obs1, obs2, rewards, dones, actions, = [], [], [], [], [],
# TODO 观测量到NN输入 的转换函数
state = obs2state(obs)
obs2.append(state.reshape((1, -1))) # for storage
obs1.append(obs.reshape((1, -1))) # for storage
if running_state is not None:
state = running_state(state, update=update_rs)
reward_episode = 0
reward_period = 0
for t in range(10000):
state_var = Variable(tensor(state).unsqueeze(0), volatile=True)
if t % 1 == 0:
if mean_action:
action = policy(state_var)[0].data[0].numpy()
else:
action = policy.select_action(state_var)[0].numpy()
rl_action = int(
action) if policy.is_disc_action else action.astype(
np.float64)
# if t%100 == 0:
# print('rl = ', rl_action)
#rl_action = np.zeros(13)
#rl_action = np.array([1,0])
rl_action = np.clip(rl_action, 0, 1)
action = CPG_transfer(rl_action, CPG_controller, obs, t)
next_state, reward, done, _ = env.step(action)
obs = next_state
# transfer
obs1.append(next_state.reshape((1, -1))) # for storage
next_state = obs2state(next_state)
obs2.append(next_state.reshape((1, -1))) # for storage
actions.append(rl_action.reshape((1, -1)))
reward_episode += reward
if running_state is not None:
next_state = running_state(next_state, update=update_rs)
if custom_reward is not None:
reward = custom_reward(state, action)
total_c_reward += reward
min_c_reward = min(min_c_reward, reward)
max_c_reward = max(max_c_reward, reward)
rewards.append(reward) # for storage
dones.append(done) # for storage
mask = 0 if done else 1
memory.push(state, rl_action, mask, next_state, reward)
if render:
env.render()
if done:
break
state = next_state
# log stats
num_steps += (t + 1)
num_episodes += 1
total_reward += reward_episode
min_reward = min(min_reward, reward_episode)
max_reward = max(max_reward, reward_episode)
# log sample data ,just for debug
if log_flag:
rewards = np.array(rewards, dtype=np.float64)
dones = np.array(dones, dtype=np.float64)
tmp = np.vstack((rewards, dones)) # states_x, states_y,
tmp1 = np.transpose(tmp)
actions = np.concatenate(actions)
obs1 = np.concatenate(obs1[:-1])
obs2 = np.concatenate(obs2[:-1])
data = np.concatenate((obs1, obs2, actions, tmp1), axis=1)
trajectory = {}
for j in range(data.shape[0]):
for i in range(data.shape[1]):
trajectory[i] = data[j][i]
logger.log(trajectory)
logger.write()
log['num_steps'] = num_steps
log['num_episodes'] = num_episodes
log['total_reward'] = total_reward
log['avg_reward'] = total_reward / num_episodes
log['max_reward'] = max_reward
log['min_reward'] = min_reward
if custom_reward is not None:
log['total_c_reward'] = total_c_reward
log['avg_c_reward'] = total_c_reward / num_steps
log['max_c_reward'] = max_c_reward
log['min_c_reward'] = min_c_reward
if queue is not None:
queue.put([pid, memory, log])
else:
return memory, log
def collect_samples(pid,
queue,
env,
p_nets,
custom_reward,
mean_action,
render,
running_state,
min_batch_size,
rsi_mem_prev=None):
torch.randn(pid)
log = dict()
memory = Memory()
team_reward = 0.0
if args.dec_agents is True:
reward_episodes = [0.0] * env.n_agents
num_steps = 0
total_reward = 0
min_reward = 1e6
max_reward = -1e6
total_c_reward = 0
min_c_reward = 1e6
max_c_reward = -1e6
num_episodes = 0
while num_steps < min_batch_size:
if args.rsi is True and rsi_mem_prev is not None:
# randomized starting point.
sp = rsi_mem_prev.rsi_state
rs = random.sample(sp, 1)
rs = rs[0]
# print(rs)
# exit()
state = env.rsi_reset({0: rs[0], 1: rs[1], 2: rs[2], 3: rs[3]})
else:
state = env.reset()
if running_state is not None:
state = running_state(state)
team_reward = 0
if args.dec_agents is True:
reward_episodes = [0.0] * env.n_agents
for t in range(10000):
state_var = tensor(state).unsqueeze(0)
action = []
with torch.no_grad():
if mean_action:
action = policy(state_var)[0][0].numpy()
else:
for i in range(env.n_agents):
action += p_nets[i].select_action(state_var)
if args.dec_agents is False:
break
next_state, reward, done, _ = env.step(action)
team_reward += sum(reward)
if args.dec_agents is True:
reward_episodes += reward
reward_episodes = [
i + j for i, j in zip(reward_episodes, reward)
]
if running_state is not None:
next_state = running_state(next_state)
if custom_reward is not None:
reward = custom_reward(state, action)
total_c_reward += reward
min_c_reward = min(min_c_reward, reward)
max_c_reward = max(max_c_reward, reward)
if args.dec_agents is False:
mask = 0 if all(done) else 1
else:
mask = [bool(1 - e) for e in done]
if args.rsi is True:
memory.push(state, action, mask, next_state, reward,
env.agent_pos)
else:
memory.push(state, action, mask, next_state, reward)
if render:
env.render()
if all(done):
break
state = next_state
# log stats
num_steps += (t + 1)
num_episodes += 1
total_reward += team_reward
min_reward = min(min_reward, team_reward)
max_reward = max(max_reward, team_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_reward'] = max_reward
log['min_reward'] = min_reward
if custom_reward is not None:
log['total_c_reward'] = total_c_reward
log['avg_c_reward'] = total_c_reward / num_steps
log['max_c_reward'] = max_c_reward
log['min_c_reward'] = min_c_reward
if queue is not None:
queue.put([pid, memory, log])
else:
return memory, log
def collect_samples(pid,
queue,
env,
policy,
custom_reward,
mean_action,
tensor,
render,
running_state,
update_rs,
min_batch_size,
seed,
thread_id,
early_stopping=False):
torch.randn(pid, )
log = dict()
if early_stopping:
training = Memory()
validation = Memory()
memory = Memory()
else:
memory = Memory()
num_steps = 0
total_reward = 0
min_reward = 1e6
max_reward = -1e6
total_c_reward = 0
min_c_reward = 1e6
max_c_reward = -1e6
num_episodes = 0
episode_rewards = []
while num_steps < min_batch_size:
#env.seed(seed + thread_id)
state = env.reset()
#print("state after env.reset():",state)
if running_state is not None:
state = running_state(state, update=update_rs)
reward_episode = 0
#
for t in range(min_batch_size - num_steps):
state_var = Variable(tensor(state).unsqueeze(0), volatile=True)
if mean_action:
action = policy(state_var)[0].data[0].numpy()
else:
action = policy.select_action(state_var)[0].numpy()
action = int(action) if policy.is_disc_action else action.astype(
np.float64)
#print("action:",action)
#get env, action and then get reward and next_state
next_state, reward, done, _ = env.step(action)
#reward sum of this episode
reward_episode += reward
if running_state is not None:
next_state = running_state(next_state, update=update_rs)
if custom_reward is not None:
reward = custom_reward(state, action)
total_c_reward += reward
min_c_reward = min(min_c_reward, reward)
max_c_reward = max(max_c_reward, reward)
mask = 0 if done else 1
if early_stopping:
ra = random.random()
if ra > 0.8 and len(validation) <= min_batch_size * 0.1:
validation.push(state, action, mask, next_state, reward)
else:
training.push(state, action, mask, next_state, reward)
else:
memory.push(state, action, mask, next_state, reward)
if render:
env.render()
if done:
break
state = next_state
# log stats
#print ('episode length:',t)
num_steps += (t + 1)
# num_episode = num_trajectories
num_episodes += 1
total_reward += reward_episode
episode_rewards.append(reward_episode)
#print("total_reward:", total_reward)
min_reward = min(min_reward, reward_episode)
max_reward = max(max_reward, reward_episode)
#print (num_steps)
log['num_steps'] = min_batch_size
log['num_episodes'] = num_episodes
log['total_reward'] = total_reward
log['episode_rewards'] = episode_rewards
log['avg_reward'] = total_reward / num_episodes
log['max_reward'] = max_reward
log['min_reward'] = min_reward
if custom_reward is not None:
log['total_c_reward'] = total_c_reward
log['avg_c_reward'] = total_c_reward / num_steps
log['max_c_reward'] = max_c_reward
log['min_c_reward'] = min_c_reward
if queue is not None:
queue.put([pid, memory, log])
else:
if early_stopping:
return memory, training, validation, log
else:
return memory, log
def collect_samples(pid, queue, env, policy, custom_reward, mean_action,
render, running_state, min_batch_size):
# def cat_s_a(s:torch.tensor, a:int):
# batch_size = 1
# label = torch.LongTensor([[a]])
# a = torch.zeros(batch_size, env.action_space.n).scatter_(1, label, 1)
# return torch.cat((s, a), 1)
def cat_s_a_np(s: np.array, a: int):
batch_size = 1
# label = np.array([[a]])
oh = np.zeros((batch_size, env.action_space.n))
oh[0, a] = 1
return np.append(s, oh)
torch.randn(pid)
log = dict()
memory = Memory()
num_steps = 0
total_reward = 0
min_reward = 1e6
max_reward = -1e6
total_c_reward = 0
min_c_reward = 1e6
max_c_reward = -1e6
num_episodes = 0
while num_steps < min_batch_size:
state = env.reset()
if running_state is not None:
state = running_state(state)
reward_episode = 0
# repeat = 0
# repeat_len = 0
stop = True
# batch_size = 1
# label = torch.LongTensor(batch_size, 1).random_() % env.action_space.n
# last_action = torch.zeros(batch_size, env.action_space.n).scatter_(1, label, 1)
last_action = 0
# ready_to_push = False
# reward_period = 0
state = cat_s_a_np(state, last_action)
# interval = 0
for t in range(10000):
state_var = tensor(state).unsqueeze(0)
# state_var = torch.cat((state_var, last_action), 1)
# state_var = cat_s_a(state_var, 1)
# Learn to stop, else maintain last action
with torch.no_grad():
if mean_action:
action = policy(state_var)[0][0].numpy()
else:
# action, repeat = policy.select_action(state_var)[0].numpy()
action, stop = policy.select_action(state_var)
action = action[0].numpy()
stop = stop[0].numpy()
# print(action)
# print(repeat)
# exit()
action = int(action) if policy.is_disc_action else action.astype(
np.float64)
stop = int(stop)
# action only updated when necessary.
if t == 0 or stop is 1:
last_action = action
# ready_to_push = True
# repeat += 1
assert (last_action is not None)
next_state, reward, done, _ = env.step(last_action)
next_state = cat_s_a_np(next_state, last_action)
reward_episode += reward
# reward_period += reward*(args.gamma**(repeat-1))
if running_state is not None:
next_state = running_state(next_state)
if custom_reward is not None:
reward = custom_reward(state, action)
total_c_reward += reward
min_c_reward = min(min_c_reward, reward)
max_c_reward = max(max_c_reward, reward)
mask = 0 if done else 1
memory.push(state, last_action, mask, next_state, reward, stop)
# if ready_to_push == True or done:
# memory.push(state, last_action, mask, next_state, reward_period, stop, repeat)
# ready_to_push = False
# repeat = 0
# reward_period = 0
# memory.push(state, last_action, mask, next_state, reward, stop)
if render:
env.render()
if done:
# print(reward_episode)
# print(t)
break
state = next_state
# log stats
num_steps += (t + 1)
num_episodes += 1
total_reward += reward_episode
min_reward = min(min_reward, reward_episode)
max_reward = max(max_reward, reward_episode)
log['num_steps'] = num_steps
log['num_episodes'] = num_episodes
log['total_reward'] = total_reward
log['avg_reward'] = total_reward / num_episodes
log['max_reward'] = max_reward
log['min_reward'] = min_reward
if custom_reward is not None:
log['total_c_reward'] = total_c_reward
log['avg_c_reward'] = total_c_reward / num_steps
log['max_c_reward'] = max_c_reward
log['min_c_reward'] = min_c_reward
if queue is not None:
queue.put([pid, memory, log])
else:
return memory, log
def collect_samples(pid,
queue,
env,
policy,
custom_reward,
mean_action,
render,
running_state,
min_batch_size,
state_only=False,
opponent_policy=None,
alpha=None,
reward_type=None):
torch.randn(pid)
log = dict()
if opponent_policy is None:
memory = Memory()
else:
memory = TwoPlayerMemory()
num_steps = 0
total_reward = 0
min_reward = 1e6
max_reward = -1e6
total_c_reward = 0
min_c_reward = 1e6
max_c_reward = -1e6
num_episodes = 0
while num_steps < min_batch_size:
state = env.reset()
if running_state is not None:
state = running_state(state)
reward_episode = 0
for t in range(100000): #range(10000):
state_var = tensor(state).unsqueeze(0)
with torch.no_grad():
if mean_action:
action = policy(state_var)[0][0].numpy()
else:
if opponent_policy is not None:
opponent_plays = np.random.choice(2,
p=[alpha, 1 - alpha])
opponent_action = opponent_policy.select_action(
state_var)[0].numpy()
player_action = policy.select_action(
state_var)[0].numpy()
if opponent_plays:
action = copy.deepcopy(opponent_action)
else:
action = copy.deepcopy(player_action)
player_action = int(
player_action
) if policy.is_disc_action else player_action.astype(
np.float64)
opponent_action = int(
opponent_action
) if policy.is_disc_action else opponent_action.astype(
np.float64)
"""if np.isnan(player_action).any():
print("Player Nan")
player_action = np.zeros_like(player_action)
if np.isnan(opponent_action).any():
print("Opponent Nan")
opponent_action = np.zeros_like(opponent_action)
action = (1 - alpha)*opponent_action.clip(-1.0, 1.0) + alpha*player_action.clip(-1.0, 1.0)"""
else:
action = policy.select_action(state_var)[0].numpy()
action = int(action) if policy.is_disc_action else action.astype(
np.float64)
if not policy.is_disc_action:
action_to_play = action.clip(-1.0, 1.0)
next_state, reward, done, _ = env.step(action_to_play)
else:
next_state, reward, done, _ = env.step(action)
reward_episode += reward
if running_state is not None:
next_state = running_state(next_state)
if custom_reward is not None:
if state_only:
reward = custom_reward(state, next_state, reward_type)
else:
reward = custom_reward(state, action, reward_type)
total_c_reward += reward
min_c_reward = min(min_c_reward, reward)
max_c_reward = max(max_c_reward, reward)
mask = 0 if done else 1
if opponent_policy is not None:
memory.push(state, player_action, opponent_action, action,
mask, next_state, reward)
else:
memory.push(state, action, mask, next_state, reward)
if render:
env.render()
if done:
if opponent_policy is not None:
memory.push(next_state, player_action, opponent_action,
action, mask, next_state, reward)
else:
memory.push(next_state, action, mask, next_state, reward)
break
state = next_state
# log stats
num_steps += (t + 1)
num_episodes += 1
total_reward += reward_episode
min_reward = min(min_reward, reward_episode)
max_reward = max(max_reward, reward_episode)
log['num_steps'] = num_steps
log['num_episodes'] = num_episodes
log['total_reward'] = total_reward
log['avg_reward'] = total_reward / num_episodes
log['max_reward'] = max_reward
log['min_reward'] = min_reward
if custom_reward is not None:
log['total_c_reward'] = total_c_reward
log['avg_c_reward'] = total_c_reward / num_steps
log['max_c_reward'] = max_c_reward
log['min_c_reward'] = min_c_reward
if queue is not None:
queue.put([pid, memory, log])
else:
return memory, log
def collect_samples(pid, queue, env, policy, custom_reward, mean_action, render, running_state, min_batch_size, horizon): torch.randn(pid) log = dict() memory = Memory() num_steps = 0 total_reward = 0 min_reward = 1e6 max_reward = -1e6 total_c_reward = 0 min_c_reward = 1e6 max_c_reward = -1e6 num_episodes = 0 while num_steps < min_batch_size: state = env.reset() if running_state is not None: state = running_state(state) reward_episode = 0 for t in range(0, horizon): state_var = tensor(state).unsqueeze(0) with torch.no_grad(): if mean_action: action = policy(state_var)[0][0].numpy() else: action = policy.select_action(state_var)[0].numpy() action = int(action) if policy.is_discrete_action else action.astype(np.float64) next_state, reward, done, _ = env.step(action) reward_episode += reward if running_state is not None: next_state = running_state(next_state) if custom_reward is not None: reward = custom_reward(state, action) total_c_reward += reward min_c_reward = min(min_c_reward, reward) max_c_reward = max(max_c_reward, reward) mask = 0 if done else 1 memory.push(state, action, mask, next_state, reward) if render: env.render() if done: break state = next_state num_steps += (t + 1) num_episodes += 1 total_reward += reward_episode min_reward = min(min_reward, reward_episode) max_reward = max(max_reward, reward_episode) log["num_steps"] = num_steps log["num_episodes"] = num_episodes log["total_reward"] = total_reward log["avg_reward"] = total_reward / num_episodes log["max_reward"] = max_reward log["min_reward"] = min_reward if custom_reward is not None: log["total_c_reward"] = total_c_reward log["avg_c_reward"] = total_c_reward / num_steps log["max_c_reward"] = max_c_reward log["min_c_reward"] = min_c_reward if queue is not None: queue.put([pid, memory, log]) else: return memory, log
def collect_samples(pid, queue, env, policy_mgr, policy_wrk, custom_reward,
mean_action, render, running_state, min_batch_size):
torch.randn(pid)
log = dict()
memory_mgr = Memory()
memory_wrk = Memory()
num_steps = 0
total_reward = 0
min_reward = 1e6
max_reward = -1e6
total_c_reward = 0
min_c_reward = 1e6
max_c_reward = -1e6
num_episodes = 0
avg_wrk_reward = 0
avg_mgr_reward = 0
mgr_steps = 0
done_count = 0
state = env.reset()
while num_steps < min_batch_size:
#state_wrk = tensor(state['observation'])
#state = np.concatenate((state['observation'],state['desired_goal']))
if running_state is not None:
state = running_state(state)
reward_episode = 0
state_mgr = tensor(
np.concatenate(
(state['observation'], state['desired_goal']))).unsqueeze(0)
with torch.no_grad():
direction = policy_mgr.select_action(state_mgr)[0]
direction = int(direction.detach().numpy())
curr_pos = state['achieved_goal']
subgoal = get_target(curr_pos, direction)
state_wrk = tensor(
np.concatenate(
(state['observation'], state['desired_goal'], subgoal)))
for t in range(10000):
with torch.no_grad():
if mean_action:
action = policy(state_var)[0][0].numpy()
else:
action = policy_wrk.select_action(
state_wrk.unsqueeze(0))[0].numpy()
next_state, reward, done, info = env.step(action)
# dist = np.linalg.norm(info['fingertip']-info['target'])
next_state_wrk = np.concatenate(
(next_state['observation'], next_state['desired_goal'],
subgoal))
reward_episode += reward
if running_state is not None:
next_state = running_state(next_state)
if custom_reward is not None:
reward = custom_reward(state, action)
total_c_reward += reward
min_c_reward = min(min_c_reward, reward)
max_c_reward = max(max_c_reward, reward)
mask_mgr = 0 if done else 1
#reward_wrk = - np.linalg.norm(subgoal - next_state['achieved_goal'])
reward_wrk = -np.linalg.norm(subgoal - next_state['achieved_goal'])
#reward_wrk = reward
subgoal_reached = (-reward_wrk < 0.05)
mask_wrk = 0 if (done or subgoal_reached) else 1
#mask_wrk = 0 if (done) else 1
memory_wrk.push(state_wrk.detach().numpy(), action, mask_wrk,
next_state_wrk, reward_wrk)
avg_wrk_reward += reward_wrk
if render:
env.render()
if (done or subgoal_reached):
#if (done):
break
state_wrk = tensor(next_state_wrk)
#next_state_mgr = np.concatenate((next_state['observation'],next_state['desired_goal']))
next_state_mgr = next_state['observation']
#reward_mgr = reward_episode - 10*np.linalg.norm(next_state['achieved_goal'] - next_state['desired_goal'])
#reward_mgr = reward_episode/50.0 - np.linalg.norm(subgoal - info['target'])
reward_mgr = reward_episode / 50.0
memory_mgr.push(
np.concatenate((state['observation'], state['desired_goal'])),
direction, mask_mgr, next_state_mgr, reward_mgr)
state = next_state
avg_mgr_reward += reward_mgr
mgr_steps += 1
# log stats
num_steps += (t + 1)
if (done):
num_episodes += 1
min_reward = min(min_reward, reward_episode)
max_reward = max(max_reward, reward_episode)
state = env.reset()
curr_pos = state['achieved_goal']
total_reward += reward_episode
else:
curr_pos = state['achieved_goal']
log['num_steps'] = num_steps
log['mgr_steps'] = mgr_steps
log['num_episodes'] = num_episodes
log['total_reward'] = total_reward
log['avg_reward'] = total_reward / (num_episodes)
log['max_reward'] = max_reward
log['min_reward'] = min_reward
log['mgr_reward'] = avg_mgr_reward / mgr_steps
log['wrk_reward'] = avg_wrk_reward / num_steps
if custom_reward is not None:
log['total_c_reward'] = total_c_reward
log['avg_c_reward'] = total_c_reward / num_steps
log['max_c_reward'] = max_c_reward
log['min_c_reward'] = min_c_reward
if queue is not None:
queue.put([pid, memory, log])
else:
return memory_mgr, memory_wrk, log
def collect_samples(pid,
queue,
env,
policy,
custom_reward,
mean_action,
render,
running_state,
min_batch_size,
randomise=False):
torch.randn(pid)
log = dict()
memory = Memory()
num_steps = 0
total_reward = 0
min_reward = 1e6
max_reward = -1e6
total_c_reward = 0
min_c_reward = 1e6
max_c_reward = -1e6
num_episodes = 0
best_index = 0
worst_index = 0
episodic_rewards = []
while num_steps < min_batch_size:
state = env.reset()
if running_state is not None:
state = running_state(state)
reward_episode = 0
for t in range(2048):
state_var = tensor(state).unsqueeze(0)
with torch.no_grad():
if mean_action:
action = policy(state_var)[0][0].numpy()
else:
action = policy.select_action(state_var)[0].numpy()
action = int(action) if policy.is_disc_action else action.astype(
np.float64)
if randomise:
action = env.action_space.sample()
next_state, reward, done, _ = env.step(action)
reward_episode += reward
if running_state is not None:
next_state = running_state(next_state)
if custom_reward is not None:
reward = custom_reward(state, action)
total_c_reward += reward
min_c_reward = min(min_c_reward, reward)
max_c_reward = max(max_c_reward, reward)
if done:
mask = 0
else:
mask = 1
memory.push(state, action, mask, next_state, reward)
if render:
env.render()
if done:
break
state = next_state
# log stats
num_steps += (t + 1)
num_episodes += 1
total_reward += reward_episode
episodic_rewards.append(reward_episode)
min_reward = min(min_reward, reward_episode)
max_reward = max(max_reward, reward_episode)
log['num_steps'] = num_steps
log['num_episodes'] = num_episodes
log['total_reward'] = total_reward
log['avg_reward'] = total_reward / num_episodes
log['max_reward'] = max_reward
log['min_reward'] = min_reward
log['episodic_rewards'] = episodic_rewards
if custom_reward is not None:
log['total_c_reward'] = total_c_reward
log['avg_c_reward'] = total_c_reward / num_steps
log['max_c_reward'] = max_c_reward
log['min_c_reward'] = min_c_reward
if queue is not None:
queue.put([pid, memory, log])
else:
return memory, log