def train():
global env_name, save_name, agent_args
env = gym.make(env_name)
if env_name == 'DobroHalfCheetah-v0':
env.unwrapped.initialize(is_render=False)
agent = Agent(env, agent_args)
v_loss_logger = Logger(save_name, 'v_loss')
p_loss_logger = Logger(save_name, 'p_loss')
score_logger = Logger(save_name, 'score')
graph = Graph(1000, save_name.upper(), agent.name)
episodes = int(5e5)
save_freq = 1
save_period = 1000
p_losses = deque(maxlen=save_period)
v_losses = deque(maxlen=save_period)
entropies = deque(maxlen=save_period)
scores = deque(maxlen=save_period)
for episode in range(episodes):
state = env.reset()
agent.actor_noise.reset()
done = False
score = 0
step = 0
while not done:
step += 1
action = agent.get_action(state, True)
next_state, reward, done, info = env.step(action)
agent.replay_memory.append([
np.array(state, np.float32), action, reward, done,
np.array(next_state, np.float32)
])
########################
if len(agent.replay_memory) > agent.train_start:
v_loss, p_loss = agent.train()
v_loss_logger.write([1, v_loss])
p_loss_logger.write([1, p_loss])
p_losses.append(p_loss)
v_losses.append(v_loss)
value = agent.get_value(state, action)
entropies.append(value)
scores.append(reward)
graph.update(np.mean(scores), np.mean(p_losses),
np.mean(v_losses), np.mean(entropies))
state = next_state
score += reward
print(episode, score, agent.epsilon)
score_logger.write([step, score])
if (episode + 1) % save_freq == 0:
agent.save()
v_loss_logger.save()
p_loss_logger.save()
score_logger.save()
graph.update(0, 0, 0, 0, finished=True)
def train():
global env, env_name
env_name = env_name.split('-')[0]
agent = Agent(env, env_name)
loss_logger = Logger(env_name, 'loss')
score_logger = Logger(env_name, 'score')
action_low = env.action_space.low[0]
action_high = env.action_space.high[0]
episodes = int(5e2)
avg_Q = deque(maxlen=200)
for episode in range(episodes):
state = env.reset()
done = False
score = 0
step = 0
while not done:
step += 1
action = agent.get_action(state)
a_t = (action/(agent.n_action-1))
a_t = a_t*(action_high - action_low) + action_low
next_state, reward, done, info = env.step([a_t])
agent.replay_memory.append([np.array(state, np.float32), action, reward, done, np.array(next_state, np.float32)])
########################
#replay 메모리에 어느정도 쌓이면 학습시작하기
if len(agent.replay_memory) > agent.train_start:
Q, loss = agent.train()
loss_logger.write([1, loss])
avg_Q.append(Q)
state = next_state
score += reward
#print(episode, accumulate+100, self.epsilon)
print(episode, score, agent.epsilon, np.mean(avg_Q))
agent.update_target_model()
score_logger.write([step, score])
if (episode+1)%agent.save_freq == 0:
agent.save()
loss_logger.save()
score_logger.save()
def train():
global env_name, save_name, agent_args
env = gym.make(env_name)
agent = Agent(env, agent_args)
v_loss_logger = Logger(save_name, 'v_loss')
cost_v_loss_logger = Logger(save_name, 'cost_v_loss')
kl_logger = Logger(save_name, 'kl')
score_logger = Logger(save_name, 'score')
cost_logger = Logger(save_name, 'cost')
graph = Graph(
1000, save_name,
['score', 'cost', 'value loss', 'cost value loss', 'kl divergence'])
max_steps = 4000
max_ep_len = 1000
episodes = int(max_steps / max_ep_len)
epochs = 500
save_freq = 10
log_length = 10
p_objectives = deque(maxlen=log_length)
c_objectives = deque(maxlen=log_length)
v_losses = deque(maxlen=log_length)
cost_v_losses = deque(maxlen=log_length)
kl_divergence = deque(maxlen=log_length)
scores = deque(maxlen=log_length * episodes)
costs = deque(maxlen=log_length * episodes)
for epoch in range(epochs):
states = []
actions = []
targets = []
cost_targets = []
gaes = []
cost_gaes = []
avg_costs = []
ep_step = 0
while ep_step < max_steps:
state = env.reset()
done = False
score = 0
cost = 0
step = 0
temp_rewards = []
temp_costs = []
values = []
cost_values = []
while True:
step += 1
ep_step += 1
assert env.observation_space.contains(state)
action, clipped_action, value, cost_value = agent.get_action(
state, True)
assert env.action_space.contains(clipped_action)
next_state, reward, done, info = env.step(clipped_action)
#for predict cost
h_dist = hazard_dist(env.hazards_pos, env.world.robot_pos())
predict_cost = get_cost(h_dist)
states.append(state)
actions.append(action)
temp_rewards.append(reward)
temp_costs.append(predict_cost)
values.append(value)
cost_values.append(cost_value)
state = next_state
score += reward
cost += info.get('cost',
0) #로그는 실제 cost를 남겨서, discrete한 cost랑 비교해야함.
if done or step >= max_ep_len:
break
if step >= max_ep_len:
action, clipped_action, value, cost_value = agent.get_action(
state, True)
else:
value = 0
cost_value = 0
print("done before max_ep_len...")
next_values = values[1:] + [value]
temp_gaes, temp_targets = agent.get_gaes_targets(
temp_rewards, values, next_values)
next_cost_values = cost_values[1:] + [cost_value]
temp_cost_gaes, temp_cost_targets = agent.get_gaes_targets(
temp_costs, cost_values, next_cost_values)
avg_costs.append(np.mean(temp_costs))
targets += list(temp_targets)
gaes += list(temp_gaes)
cost_targets += list(temp_cost_targets)
cost_gaes += list(temp_cost_gaes)
score_logger.write([step, score])
cost_logger.write([step, cost])
scores.append(score)
costs.append(cost)
trajs = [
states, actions, targets, cost_targets, gaes, cost_gaes, avg_costs
]
v_loss, cost_v_loss, p_objective, cost_objective, kl = agent.train(
trajs)
v_loss_logger.write([ep_step, v_loss])
cost_v_loss_logger.write([ep_step, cost_v_loss])
kl_logger.write([ep_step, kl])
p_objectives.append(p_objective)
c_objectives.append(cost_objective)
v_losses.append(v_loss)
cost_v_losses.append(cost_v_loss)
kl_divergence.append(kl)
print(np.mean(scores), np.mean(costs), np.mean(v_losses),
np.mean(cost_v_losses), np.mean(kl_divergence),
np.mean(c_objectives))
graph.update([
np.mean(scores),
np.mean(costs),
np.mean(v_losses),
np.mean(cost_v_losses),
np.mean(kl_divergence)
])
if (epoch + 1) % save_freq == 0:
agent.save()
v_loss_logger.save()
cost_v_loss_logger.save()
kl_logger.save()
score_logger.save()
cost_logger.save()
graph.update(None, finished=True)
def train():
global env_name, save_name, agent_args, env_real, env_sim, nets
env_real = env_real.Env_real(False)
env_sim = env_sim.Env_sim(True)
GAT_model = nets.GAT_net(env_real, env_sim, GAT_args)
agent = Agent(env_sim, agent_args)
# wandb.init(project=save_name)
accum_step = 0
avg_temp_cost = 0
v_loss_logger = Logger(save_name, 'v_loss')
cost_v_loss_logger = Logger(save_name, 'cost_v_loss')
kl_logger = Logger(save_name, 'kl')
score_logger = Logger(save_name, 'score')
cost_logger = Logger(save_name, 'cost')
max_steps = 2000
max_ep_len = 1000
episodes = int(max_steps / max_ep_len)
epochs = 2 #50
save_freq = 1
log_length = 10
p_objectives = deque(maxlen=log_length)
c_objectives = deque(maxlen=log_length)
v_losses = deque(maxlen=log_length)
cost_v_losses = deque(maxlen=log_length)
kl_divergence = deque(maxlen=log_length)
scores = deque(maxlen=log_length * episodes)
costs = deque(maxlen=log_length * episodes)
is_backup = False
backup_name = '{}/backup.pkl'.format(save_name)
if os.path.isfile(backup_name):
#input_value = raw_input('backup file exists. wanna continue the last work?( y/n )')
#if input_value != 'n':
# is_backup = True
is_backup = True
if is_backup:
with open(backup_name, 'rb') as f:
backup_list = pickle.load(f)
start_iter = backup_list[0]
else:
start_iter = 0
backup_list = [start_iter]
for epoch in range(start_iter, epochs):
#continue?
print("=" * 20)
print("Epoch : {}".format(epoch + 1))
#input_value = raw_input("wanna continue episodes?( y/n )")
#if input_value == 'n':
# break
states = []
actions = []
targets = []
cost_targets = []
gaes = []
cost_gaes = []
avg_costs = []
ep_step = 0
while ep_step < max_steps:
#input_value = raw_input("ready?")
state = env_sim.reset()
done = False
score = 0
cost = 0
step = 0
temp_rewards = []
temp_costs = []
values = []
cost_values = []
while True:
if rospy.is_shutdown():
sys.exit()
step += 1
ep_step += 1
action, clipped_action, value, cost_value = agent.get_action(
state, True)
# action transformer by GAT
transformed_next_state = GAT_model.forward_transform(
state, clipped_action)
transformed_action = GAT_model.backward_transform(
state, transformed_next_state)
next_state, reward, done, info = env_sim.step(
transformed_action)
predict_cost = info['continuous_cost']
states.append(state)
actions.append(action)
temp_rewards.append(reward)
temp_costs.append(predict_cost)
values.append(value)
cost_values.append(cost_value)
state = next_state
score += reward
cost += info.get('cost', 0)
if done or step >= max_ep_len:
break
print("step : {}, score : {}".format(step, score))
if step >= max_ep_len:
action, clipped_action, value, cost_value = agent.get_action(
state, True)
else:
value = 0
cost_value = 0
print("done before max_ep_len...")
next_values = values[1:] + [value]
temp_gaes, temp_targets = agent.get_gaes_targets(
temp_rewards, values, next_values)
next_cost_values = cost_values[1:] + [cost_value]
temp_cost_gaes, temp_cost_targets = agent.get_gaes_targets(
temp_costs, cost_values, next_cost_values)
avg_costs.append(np.mean(temp_costs))
targets += list(temp_targets)
gaes += list(temp_gaes)
cost_targets += list(temp_cost_targets)
cost_gaes += list(temp_cost_gaes)
score_logger.write([step, score])
cost_logger.write([step, cost])
scores.append(score)
costs.append(cost)
accum_step += step
avg_temp_cost = np.mean(temp_costs)
# wandb.log({'step': accum_step, 'score':score, 'cost':cost, 'avg_temp_cost':avg_temp_cost})
trajs = [
states, actions, targets, cost_targets, gaes, cost_gaes, avg_costs
]
v_loss, cost_v_loss, p_objective, cost_objective, kl = agent.train(
trajs)
v_loss_logger.write([ep_step, v_loss])
cost_v_loss_logger.write([ep_step, cost_v_loss])
kl_logger.write([ep_step, kl])
p_objectives.append(p_objective)
c_objectives.append(cost_objective)
v_losses.append(v_loss)
cost_v_losses.append(cost_v_loss)
kl_divergence.append(kl)
print(np.mean(scores), np.mean(costs), np.mean(v_losses),
np.mean(cost_v_losses), np.mean(kl_divergence),
np.mean(c_objectives))
if (epoch + 1) % save_freq == 0:
agent.save()
v_loss_logger.save()
cost_v_loss_logger.save()
kl_logger.save()
score_logger.save()
cost_logger.save()
#backup
backup_list[0] = epoch + 1
with open(backup_name, 'wb') as f:
pickle.dump(backup_list, f)
def train():
global env_name, save_name, agent_args
env = gym.make(env_name)
agent = Agent(env, agent_args)
p_loss_logger = Logger(save_name, 'p_loss')
v_loss_logger = Logger(save_name, 'v_loss')
kl_logger = Logger(save_name, 'kl')
score_logger = Logger(save_name, 'score')
graph = Graph(
1000, save_name,
['score', 'policy loss', 'value loss', 'kl divergence', 'entropy'])
episodes = 10
max_steps = 4000
max_ep_len = min(1000, env.spec.max_episode_steps)
epochs = int(1e5)
save_freq = 10
save_period = 10
p_losses = deque(maxlen=save_period)
v_losses = deque(maxlen=save_period)
kl_divergence = deque(maxlen=save_period)
entropies = deque(maxlen=save_period)
scores = deque(maxlen=save_period * episodes)
for epoch in range(epochs):
states = []
actions = []
targets = []
next_states = []
rewards = []
gaes = []
ep_step = 0
#for episode in range(episodes):
while ep_step < max_steps:
state = env.reset()
done = False
score = 0
step = 0
temp_rewards = []
values = []
while True:
step += 1
ep_step += 1
action, clipped_action, value = agent.get_action(state, True)
next_state, reward, done, info = env.step(clipped_action)
states.append(state)
actions.append(action)
temp_rewards.append(reward)
next_states.append(next_state)
rewards.append(reward)
values.append(value)
state = next_state
score += reward
if done or step >= max_ep_len:
break
if step >= max_ep_len:
action, clipped_action, value = agent.get_action(state, True)
else: #중간에 끝난 거면, 다 돌기전에 죽어버린거니, value = 0 으로 해야함
value = 0
print("done before max_ep_len...")
next_values = values[1:] + [value]
temp_gaes, temp_targets = agent.get_gaes_targets(
temp_rewards, values, next_values)
targets += list(temp_targets)
gaes += list(temp_gaes)
score_logger.write([step, score])
scores.append(score)
trajs = [states, actions, targets, next_states, rewards, gaes]
p_loss, v_loss, kl, entropy = agent.train(trajs)
p_loss_logger.write([ep_step, p_loss])
v_loss_logger.write([ep_step, v_loss])
kl_logger.write([ep_step, kl])
p_losses.append(p_loss)
v_losses.append(v_loss)
kl_divergence.append(kl)
entropies.append(entropy)
print(np.mean(scores), np.mean(p_losses), np.mean(v_losses),
np.mean(kl_divergence), np.mean(entropies))
graph.update([
np.mean(scores),
np.mean(p_losses),
np.mean(v_losses),
np.mean(kl_divergence),
np.mean(entropies)
])
if (epoch + 1) % save_freq == 0:
agent.save()
p_loss_logger.save()
v_loss_logger.save()
kl_logger.save()
score_logger.save()
graph.update(None, finished=True)
def train():
global env_name, save_name, agent_args
env = gym.make(env_name)
env.unwrapped.initialize(is_render=False)
agent = Agent(env, agent_args)
v_loss_logger = Logger(save_name, 'v_loss')
p_loss_logger = Logger(save_name, 'p_loss')
score_logger = Logger(save_name, 'score')
graph = Graph(1000, save_name.upper(), agent.name)
episodes = 10
epochs = int(1e5)
save_freq = 10
save_period = 100
p_losses = deque(maxlen=save_period)
v_losses = deque(maxlen=save_period)
entropies = deque(maxlen=save_period)
scores = deque(maxlen=save_period * episodes)
for epoch in range(epochs):
states = []
actions = []
targets = []
ep_step = 0
for episode in range(episodes):
state = env.reset()
done = False
score = 0
step = 0
temp_rewards = []
while not done:
step += 1
ep_step += 1
action, clipped_action = agent.get_action(state, True)
next_state, reward, done, info = env.step(clipped_action)
states.append(state)
actions.append(action)
temp_rewards.append(reward)
state = next_state
score += reward
score_logger.write([step, score])
scores.append(score)
temp_targets = np.zeros_like(temp_rewards)
ret = 0
for t in reversed(range(len(temp_rewards))):
ret = temp_rewards[t] + agent.discount_factor * ret
temp_targets[t] = ret
targets += list(temp_targets)
trajs = [states, actions, targets]
v_loss, p_objective, kl = agent.train(trajs)
v_loss_logger.write([ep_step, v_loss])
p_loss_logger.write([ep_step, p_objective])
p_losses.append(p_objective)
v_losses.append(v_loss)
entropies.append(kl)
#print(v_loss, p_objective, kl)
print(np.mean(scores), np.mean(p_losses), np.mean(v_losses),
np.mean(entropies))
graph.update(np.mean(scores), np.mean(p_losses), np.mean(v_losses),
np.mean(entropies))
if (epoch + 1) % save_freq == 0:
agent.save()
v_loss_logger.save()
p_loss_logger.save()
score_logger.save()
graph.update(0, 0, 0, 0, finished=True)
def train():
global env_name, save_name, agent_args
env = gym.make(env_name)
agent = Agent(env, agent_args)
score_logger = Logger(save_name, 'score')
graph = Graph(1000, save_name,
['score', 'policy loss', 'Q value loss', 'entropy'])
max_steps = 4000
max_ep_len = min(1000, env.spec.max_episode_steps)
start_training_after_steps = 1000
step_per_training = 50
epochs = 1000
save_freq = 1
record_length = 10
p_losses = deque(maxlen=record_length *
int(max_ep_len / step_per_training))
q_losses = deque(maxlen=record_length *
int(max_ep_len / step_per_training))
entropies = deque(maxlen=record_length *
int(max_ep_len / step_per_training))
scores = deque(maxlen=record_length)
total_step = 0
for epoch in range(epochs):
ep_step = 0
while ep_step < max_steps:
state = env.reset()
score = 0
step = 0
while True:
step += 1
ep_step += 1
total_step += 1
action = agent.get_action(state, True)
next_state, reward, done, info = env.step(action)
done = False if step >= max_ep_len else done
agent.replay_memory.append(
[state, action, reward,
np.float(done), next_state])
if len(agent.replay_memory) > start_training_after_steps and (
total_step + 1) % step_per_training == 0:
for _ in range(step_per_training):
p_loss, q_loss, entropy = agent.train()
p_losses.append(p_loss)
q_losses.append(q_loss)
entropies.append(entropy)
print(np.mean(scores), np.mean(p_losses),
np.mean(q_losses), np.mean(entropies))
state = next_state
score += reward
if done or step >= max_ep_len:
break
score_logger.write([step, score])
scores.append(score)
graph.update([
np.mean(scores),
np.mean(p_losses),
np.mean(q_losses),
np.mean(entropies)
])
if (epoch + 1) % save_freq == 0:
agent.save()
score_logger.save()
graph.update(None, finished=True)