def process__buffer(q_aggr, qs_dist, args, **_kwargs):
max_memo = args.max_memo
env_name = args.env_name
max_step = args.max_step
batch_size = args.batch_size
repeat_times = 2
# reward_scale = args.reward_scale
# gamma = args.gamma
'''init'''
env = gym.make(env_name)
state_dim, action_dim, max_action, target_reward, is_discrete = get_env_info(env, is_print=False)
buffer = BufferArray(max_memo, state_dim, action_dim) # experiment replay buffer
workers_num = len(qs_dist)
'''loop'''
is_training = True
while is_training:
for i in range(workers_num):
memo_array, is_solved = q_aggr.get()
buffer.extend_memo(memo_array)
if is_solved:
is_training = False
buffer.init_before_sample()
for i in range(max_step * repeat_times):
# batch_arrays = buffer.random_sample(batch_size, device=None) # faster but worse
for q_dist in qs_dist:
batch_arrays = buffer.random_sample(batch_size, device=None) # slower but better
q_dist.put(batch_arrays)
print('|| Exit: process__buffer')
def process__buffer(q_aggr, qs_dist, args, **_kwargs):
max_memo = args.max_memo
env_name = args.env_name
max_step = args.max_step
batch_size = args.batch_size
repeat_times = 2
reward_scale = args.reward_scale
gamma = args.gamma
'''init'''
env = gym.make(env_name)
state_dim, action_dim, max_action, target_reward = get_env_info(
env, be_quiet=False)
buffer = BufferArray(max_memo, state_dim,
action_dim) # experiment replay buffer
workers_num = len(qs_dist)
'''loop'''
with torch.no_grad(): # update replay buffer
# rewards, steps = agent.update_buffer(
# env, buffer, max_step, max_action, reward_scale, gamma)
rewards, steps = initial_exploration(env, buffer, max_step, max_action,
reward_scale, gamma, action_dim)
while True:
for _ in range(workers_num):
memo_array = q_aggr.get()
buffer.extend_memo(memo_array)
buffer.init_before_sample()
for _ in range(max_step * repeat_times):
for q_dist in qs_dist:
batch_arrays = buffer.random_sample(batch_size, device=None)
q_dist.put(batch_arrays)
def run__tutorial_discrete_action():
"""It is a DQN tutorial, we need 1min for training.
This simplify DQN can't work well on harder task.
Other RL algorithms can work well on harder task but complicated.
You can change this code and make the training finish in (10 sec, 10k step) as an execrise.
"""
env_name = 'CartPole-v0' # a tutorial RL env. We need 10s for training.
env = gym.make(env_name) # an OpenAI standard env
state_dim = 4
action_dim = 2
action_max = int(1)
target_reward = 195.0
is_discrete = True
# from AgentRun import get_env_info
# state_dim, action_dim, max_action, target_reward, is_discrete = get_env_info(env, is_print=True)
# assert is_discrete is True # DQN is for discrete action space.
""" You will see the following:
| env_name: <CartPoleEnv<CartPole-v0>>, action space: Discrete
| state_dim: 4, action_dim: 2, action_max: 1, target_reward: 195.0
"""
''' I copy the code from AgentDQN to the following for tutorial.'''
net_dim = 2**7 # the dimension (or width) of network
learning_rate = 2e-4 # learning rate for Adam Optimizer (ADAM = RMSProp + Momentum)
max_buffer = 2**12 # the max storage number of replay buffer.
max_epoch = 2**12 # epoch or episodes when training step
max_step = 2**9 # the max step that actor interact with env before training critic
gamma = 0.99 # reward discount factor (gamma must less than 1.0)
batch_size = 2**6 # batch_size for network training
criterion = torch.nn.MSELoss() # criterion for critic's q_value estimate
device = torch.device("cuda" if torch.cuda.is_available() else
"cpu") # choose GPU or CPU automatically
''' QNet is an actor or critic? DQN is not a Actor-Critic Method.
AgentDQN chooses action with the largest q value outputing by Q_Network. Q_Network is an actor.
AgentDQN outputs q_value by Q_Network. Q_Network is also a critic.
'''
act = QNet(state_dim, action_dim, net_dim).to(device)
act.train()
act_optim = torch.optim.Adam(act.parameters(), lr=learning_rate)
act_target = QNet(state_dim, action_dim, net_dim).to(device)
act_target.load_state_dict(act.state_dict())
act_target.eval()
# from AgentRun import BufferList # simpler but slower
from AgentZoo import BufferArray # faster but a bit complicated
buffer = BufferArray(
max_buffer, state_dim,
action_dim=1) # experiment replay buffer, discrete action is an int
'''training loop'''
self_state = env.reset()
self_steps = 0 # steps of an episode
self_r_sum = 0.0 # sum of rewards of an episode with exploration
total_step = 0 # total step before training st0p
evaluator = EvaluateRewardSV(env) # SV: Simplify Version for tutorial
max_reward = evaluator.get_eva_reward__sv(act, max_step, action_max,
is_discrete)
# the max r_sum without exploration
start_time = time.time()
for epoch in range(max_epoch):
'''update_buffer'''
explore_rate = 0.1 # explore rate when update_buffer(), epsilon-greedy
rewards = list()
steps = list()
for _ in range(max_step):
if rd.rand(
) < explore_rate: # epsilon-Greedy: explored policy for DQN
action = rd.randint(action_dim)
else:
states = torch.tensor((self_state, ),
dtype=torch.float32,
device=device)
actions = act_target(states).argmax(
dim=1).cpu().data.numpy() # discrete action space
action = actions[0]
next_state, reward, done, _ = env.step(action)
self_r_sum += reward
self_steps += 1
mask = 0.0 if done else gamma
buffer.add_memo((reward, mask, self_state, action, next_state))
self_state = next_state
if done:
rewards.append(self_r_sum)
self_r_sum = 0.0
steps.append(self_steps)
self_steps = 0
self_state = env.reset()
total_step += sum(steps)
avg_reward = np.average(rewards)
print(end=f'Reward:{avg_reward:6.1f} Step:{total_step:8} ')
'''update_parameters'''
loss_c_sum = 0.0
update_times = max_step
buffer.init_before_sample() # update the buffer.now_len
for _ in range(update_times):
with torch.no_grad():
rewards, masks, states, actions, next_states = buffer.random_sample(
batch_size, device)
next_q_target = act_target(next_states).max(dim=1,
keepdim=True)[0]
q_target = rewards + masks * next_q_target
act.train()
actions = actions.type(torch.long)
q_eval = act(states).gather(1, actions)
critic_loss = criterion(q_eval, q_target)
loss_c_sum += critic_loss.item()
act_optim.zero_grad()
critic_loss.backward()
act_optim.step()
soft_target_update(act_target, act, tau=5e-2)
# soft_target_update(act_target, act, tau=5e-3)
''' A small tau can stabilize training in harder env.
You can change tau into smaller tau 5e-3. But this env is too easy.
You can try the harder env and other DRL Algorithms in run__xx() in AgentRun.py
'''
# loss_a_avg = 0.0
loss_c_avg = loss_c_sum / update_times
print(end=f'Loss:{loss_c_avg:6.1f} ')
# evaluate the true reward of this agent without exploration
eva_reward_list = [
evaluator.get_eva_reward__sv(act, max_step, action_max,
is_discrete) for _ in range(3)
]
eva_reward = np.average(eva_reward_list)
print(f'TrueRewward:{eva_reward:6.1f}')
if eva_reward > max_reward:
max_reward = eva_reward
if max_reward > target_reward:
print(
f"|\tReach target_reward: {max_reward:6.1f} > {target_reward:6.1f}"
)
break
used_time = int(time.time() - start_time)
print(f"|\tTraining UsedTime: {used_time}s")
def run__tutorial_continuous_action():
"""It is a DDPG tutorial, we need about 300s for training.
I hate OU Process because of its lots of hyper-parameters. So this DDPG has no OU Process.
This simplify DDPG can't work well on harder task.
Other RL algorithms can work well on harder task but complicated.
You can change this code and make the training finish in 100s.
"""
env_name = 'Pendulum-v0' # a tutorial RL env. We need 300s for training.
env = gym.make(env_name) # an OpenAI standard env
state_dim = 3
action_dim = 1
action_max = 2.0
target_reward = -100.0
is_discrete = False
# from AgentRun import get_env_info
# state_dim, action_dim, max_action, target_reward, is_discrete = get_env_info(
# env, is_print=True, target_reward=-100.0)
# assert is_discrete is False # DDPG is for discrete action space.
""" You will see the following:
| env_name: <PendulumEnv<Pendulum-v0>>, action space: Continuous
| state_dim: 3, action_dim: 1, action_max: 2.0, target_reward: 0.0
"""
''' I copy the code from AgentDQN to the following for tutorial.'''
net_dim = 2**5 # the dimension (or width) of network
learning_rate = 2e-4 # learning rate for Adam Optimizer (ADAM = RMSProp + Momentum)
max_buffer = 2**14 # the max storage number of replay buffer.
max_epoch = 2**12 # epoch or episodes when training step
max_step = 2**8 # the max step that actor interact with env before training critic
gamma = 0.99 # reward discount factor (gamma must less than 1.0)
batch_size = 2**7 # batch_size for network training
update_freq = 2**7
criterion = torch.nn.SmoothL1Loss(
) # criterion for critic's q_value estimate
device = torch.device("cuda" if torch.cuda.is_available() else
"cpu") # choose GPU or CPU automatically
act_dim = net_dim
act = Actor(state_dim, action_dim, act_dim).to(device)
act.train()
act_optim = torch.optim.Adam(act.parameters(), lr=learning_rate)
act_target = Actor(state_dim, action_dim, act_dim).to(device)
act_target.load_state_dict(act.state_dict())
act_target.eval()
cri_dim = int(net_dim * 1.25)
cri = Critic(state_dim, action_dim, cri_dim).to(device)
cri.train()
cri_optim = torch.optim.Adam(cri.parameters(), lr=learning_rate)
cri_target = Critic(state_dim, action_dim, cri_dim).to(device)
cri_target.load_state_dict(cri.state_dict())
cri_target.eval()
# from AgentRun import BufferList # simpler but slower
from AgentZoo import BufferArray # faster but a bit complicated
buffer = BufferArray(max_buffer, state_dim,
action_dim) # experiment replay buffer
'''training loop'''
self_state = env.reset()
self_steps = 0 # the steps of an episode
self_r_sum = 0.0 # the sum of rewards of an episode with exploration
total_step = 0
explore_noise = 0.05
evaluator = EvaluateRewardSV(env) # SV: Simplify Version for tutorial
max_reward = evaluator.get_eva_reward__sv(act, max_step, action_max,
is_discrete)
# the max r_sum without exploration
start_time = time.time()
while total_step < max_step: # collect buffer before training
for _ in range(max_step):
action = rd.uniform(-1, 1, size=action_dim)
next_state, reward, done, _ = env.step(action * action_max)
mask = 0.0 if done else gamma
buffer.add_memo((reward, mask, self_state, action, next_state))
total_step += 1
if done:
self_state = env.reset()
break
self_state = next_state
for epoch in range(max_epoch):
'''update_buffer'''
explore_rate = 0.5 # explore rate when update_buffer(), epsilon-greedy
reward_list = list()
step_list = list()
for _ in range(max_step):
states = torch.tensor((self_state, ),
dtype=torch.float32,
device=device)
actions = act_target(
states).cpu().data.numpy() # discrete action space
action = actions[0]
if rd.rand() < explore_rate:
action = rd.normal(action, explore_noise).clip(-1, +1)
next_state, reward, done, _ = env.step(action * action_max)
self_r_sum += reward
self_steps += 1
mask = 0.0 if done else gamma
buffer.add_memo((reward, mask, self_state, action, next_state))
self_state = next_state
if done:
reward_list.append(self_r_sum)
self_r_sum = 0.0
step_list.append(self_steps)
self_steps = 0
self_state = env.reset()
total_step += sum(step_list)
avg_reward = np.average(reward_list)
print(end=f'Reward:{avg_reward:8.1f} Step:{total_step:8} ')
'''update_parameters'''
loss_a_sum = 0.0
loss_c_sum = 0.0
update_times = max_step
buffer.init_before_sample() # update the buffer.now_len
for i in range(update_times):
for _ in range(2): # Two Time-scale Update Rule (TTUR)
with torch.no_grad():
reward, mask, state, action, next_state = buffer.random_sample(
batch_size, device)
next_action = act_target(next_state)
next_q_target = cri_target(next_state, next_action)
q_target = reward + mask * next_q_target
q_eval = cri(state, action)
critic_loss = criterion(q_eval, q_target)
loss_c_sum += critic_loss.item()
cri_optim.zero_grad()
critic_loss.backward()
cri_optim.step()
action_pg = act(state) # policy gradient
actor_loss = -cri(state, action_pg).mean() # policy gradient
loss_a_sum += actor_loss.item()
act_optim.zero_grad()
actor_loss.backward()
act_optim.step()
'''soft target update'''
# soft_target_update(cri_target, cri, tau=5e-3)
# soft_target_update(act_target, act, tau=5e-3)
'''hard target update'''
if i % update_freq == 0:
cri_target.load_state_dict(cri.state_dict())
act_target.load_state_dict(act.state_dict())
loss_c_avg = loss_c_sum / (update_times * 2)
loss_a_avg = loss_a_sum / update_times
print(end=f'LossC:{loss_c_avg:6.1f} LossA:{loss_a_avg:6.1f} ')
# evaluate the true reward of this agent without exploration
eva_reward_list = [
evaluator.get_eva_reward__sv(act, max_step, action_max,
is_discrete) for _ in range(3)
]
eva_reward = np.average(eva_reward_list)
print(f'TrueRewward:{eva_reward:8.1f}')
if eva_reward > max_reward:
max_reward = eva_reward
if max_reward > target_reward:
print(
f"|\tReach target_reward: {max_reward:6.1f} > {target_reward:6.1f}"
)
break
used_time = int(time.time() - start_time)
print(f"|\tTraining UsedTime: {used_time}s")