replay_buffer_size = 5e5
replay_buffer = ReplayBuffer(replay_buffer_size)
qt_opt = QT_Opt(replay_buffer, hidden_dim)
if args.train:
# hyper-parameters
max_episodes = 2000
max_steps = 20 if ENV == 'Reacher' else 150 # Pendulum needs 150 steps per episode to learn well, cannot handle 20
frame_idx = 0
episode_rewards = []
for i_episode in range(max_episodes):
if ENV == 'Reacher':
state = env.reset(SCREEN_SHOT)
elif ENV == 'Pendulum':
state = env.reset()
episode_reward = 0
for step in range(max_steps):
# action = qt_opt.policy.act(state)
action = qt_opt.cem_optimal_action(state)
if ENV == 'Reacher':
next_state, reward, done, _ = env.step(
action, SPARSE_REWARD, SCREEN_SHOT)
elif ENV == 'Pendulum':
next_state, reward, done, _ = env.step(action)
env.render()
episode_reward += reward
replay_buffer.push(state, action, reward, next_state, done)
def worker(id, sac_trainer, ENV, rewards_queue, replay_buffer, max_episodes, max_steps, batch_size,
explore_steps, \
update_itr, action_itr, AUTO_ENTROPY, DETERMINISTIC, hidden_dim, model_path):
'''
the function for sampling with multi-processing
'''
with torch.cuda.device(id % torch.cuda.device_count()):
sac_trainer.to_cuda()
print(sac_trainer, replay_buffer) # sac_tainer are not the same, but all networks and optimizers in it are the same; replay buffer is the same one.
if ENV == 'Reacher':
NUM_JOINTS=2
LINK_LENGTH=[200, 140]
INI_JOING_ANGLES=[0.1, 0.1]
SCREEN_SIZE=1000
SPARSE_REWARD=False
SCREEN_SHOT=False
action_range = 10.0
env=Reacher(screen_size=SCREEN_SIZE, num_joints=NUM_JOINTS, link_lengths = LINK_LENGTH, \
ini_joint_angles=INI_JOING_ANGLES, target_pos = [369,430], render=True, change_goal=False)
action_dim = env.num_actions
state_dim = env.num_observations
elif ENV == 'Pendulum':
env = NormalizedActions(gym.make("Pendulum-v0"))
action_dim = env.action_space.shape[0]
state_dim = env.observation_space.shape[0]
action_range=1.
frame_idx=0
rewards=[]
# training loop
for eps in range(max_episodes):
episode_reward = 0
if ENV == 'Reacher':
state = env.reset(SCREEN_SHOT)
elif ENV == 'Pendulum':
state = env.reset()
for step in range(max_steps):
if frame_idx > explore_steps:
action = sac_trainer.policy_net.get_action(state, deterministic = DETERMINISTIC)
else:
action = sac_trainer.policy_net.sample_action()
try:
if ENV == 'Reacher':
next_state, reward, done, _ = env.step(action, SPARSE_REWARD, SCREEN_SHOT)
elif ENV == 'Pendulum':
next_state, reward, done, _ = env.step(action)
env.render()
except KeyboardInterrupt:
print('Finished')
sac_trainer.save_model(model_path)
replay_buffer.push(state, action, reward, next_state, done)
state = next_state
episode_reward += reward
frame_idx += 1
# if len(replay_buffer) > batch_size:
if replay_buffer.get_length() > batch_size:
for i in range(update_itr):
_ = sac_trainer.update(batch_size, reward_scale=10., auto_entropy=AUTO_ENTROPY,
target_entropy=-1. * action_dim)
if eps % 10 == 0 and eps > 0:
# plot(rewards, id)
sac_trainer.save_model(model_path)
if done:
break
print('Worker: ', id, '| Episode: ', eps, '| Episode Reward: ', episode_reward)
# if len(rewards) == 0:
# rewards.append(episode_reward)
# else:
# rewards.append(rewards[-1] * 0.9 + episode_reward * 0.1)
rewards_queue.put(episode_reward)
sac_trainer.save_model(model_path)
torch.autograd.set_detect_anomaly(True)
alg = DDPG(replay_buffer, state_space, action_space, hidden_dim)
if args.train:
# alg.load_model(model_path)
# hyper-parameters
max_episodes = 1000
max_steps = 100
frame_idx = 0
rewards=[]
for i_episode in range (max_episodes):
q_loss_list=[]
policy_loss_list=[]
state = env.reset()
episode_reward = 0
for step in range(max_steps):
if frame_idx > explore_steps:
action = alg.policy_net.get_action(state)
else:
action = alg.policy_net.sample_action()
next_state, reward, done, _ = env.step(action)
if ENV !='Reacher':
env.render()
replay_buffer.push(state, action, reward, next_state, done)
state = next_state
episode_reward += reward
frame_idx += 1
def worker(id, ): # thread could read global variables
'''
the function for sampling with multi-threading
'''
print(sac_trainer, replay_buffer)
if ENV == 'Reacher':
env=Reacher(screen_size=SCREEN_SIZE, num_joints=NUM_JOINTS, link_lengths = LINK_LENGTH, \
ini_joint_angles=INI_JOING_ANGLES, target_pos = [369,430], render=True, change_goal=False)
elif ENV == 'Pendulum':
env = NormalizedActions(gym.make("Pendulum-v0"))
print(env)
frame_idx = 0
rewards = []
# training loop
for eps in range(max_episodes):
episode_reward = 0
if ENV == 'Reacher':
state = env.reset(SCREEN_SHOT)
elif ENV == 'Pendulum':
state = env.reset()
for step in range(max_steps):
if frame_idx > explore_steps:
action = sac_trainer.policy_net.get_action(
state, deterministic=DETERMINISTIC)
else:
action = sac_trainer.policy_net.sample_action()
try:
if ENV == 'Reacher':
next_state, reward, done, _ = env.step(
action, SPARSE_REWARD, SCREEN_SHOT)
elif ENV == 'Pendulum':
next_state, reward, done, _ = env.step(action)
env.render()
except KeyboardInterrupt:
print('Finished')
sac_trainer.save_model(model_path)
replay_buffer.push(state, action, reward, next_state, done)
state = next_state
episode_reward += reward
frame_idx += 1
if len(replay_buffer) > batch_size:
for i in range(update_itr):
_ = sac_trainer.update(batch_size,
reward_scale=10.,
auto_entropy=AUTO_ENTROPY,
target_entropy=-1. * action_dim)
if eps % 10 == 0 and eps > 0:
plot(rewards, id)
sac_trainer.save_model(model_path)
if done:
break
print('Episode: ', eps, '| Episode Reward: ', episode_reward)
# if len(rewards) == 0: rewards.append(episode_reward)
# else: rewards.append(rewards[-1]*0.9+episode_reward*0.1)
sac_trainer.save_model(model_path)
NUM_JOINTS = 2
LINK_LENGTH = [200, 140]
INI_JOING_ANGLES = [0.1, 0.1]
SCREEN_SIZE = 1000
SPARSE_REWARD = False
SCREEN_SHOT = False
DETERMINISTIC = False
env=Reacher(screen_size=SCREEN_SIZE, num_joints=NUM_JOINTS, link_lengths = LINK_LENGTH, \
ini_joint_angles=INI_JOING_ANGLES, target_pos = [369,430], render=True)
ppo = PPO()
if args.train:
all_ep_r = []
for ep in range(EP_MAX):
s = env.reset(SCREEN_SHOT)
s = s / 100.
buffer_s, buffer_a, buffer_r = [], [], []
ep_r = 0
for t in range(EP_LEN): # in one episode
# env.render()
a = ppo.choose_action(s)
s_, r, done, distance2goal = env.step(a, SPARSE_REWARD,
SCREEN_SHOT)
s_ = s_ / 100.
buffer_s.append(s)
buffer_a.append(a)
# print('r, norm_r: ', r, (r+8)/8)
'''the normalization makes reacher's reward almost same and not work'''
# buffer_r.append((r+8)/8) # normalize reward, find to be useful
buffer_r.append(r)
