def train_agent_ppo(
agent_class,
env_name,
cwd,
net_dim,
max_step,
max_memo,
max_epoch, # env
batch_size,
gamma,
**_kwargs): # 2020-0430
env = gym.make(env_name)
state_dim, action_dim, max_action, target_reward = get_env_info(env)
agent = agent_class(state_dim, action_dim, net_dim)
agent.save_or_load_model(cwd, is_save=False)
# memo_action_dim = action_dim if max_action else 1 # Discrete action space
# memo = Memories(max_memo, memo_dim=1 + 1 + state_dim + memo_action_dim + state_dim)
# memo.save_or_load_memo(cwd, is_save=False)
state_norm = AutoNormalization((state_dim, ), clip=5.0)
recorder = Recorder(agent,
max_step,
max_action,
target_reward,
env_name,
state_norm=state_norm)
# r_norm = RewardNorm(n_max=target_reward, n_min=recorder.reward_avg)
try:
for epoch in range(max_epoch):
with torch.no_grad(): # just the GPU memory
rewards, steps, memory = agent.inactive_in_env_ppo(
env, max_step, max_memo, max_action, state_norm)
l_total, l_value = agent.update_parameter_ppo(memory,
batch_size,
gamma,
ep_ratio=1 -
epoch / max_epoch)
if np.isnan(l_total) or np.isnan(l_value):
print(
"ValueError: loss value should not be 'nan'. Please run again."
)
return False
with torch.no_grad(): # for saving the GPU memory
recorder.show_reward(epoch, rewards, steps, l_value, l_total)
is_solved = recorder.check_reward(cwd, l_value, l_total)
if is_solved:
break
except KeyboardInterrupt:
print("raise KeyboardInterrupt while training.")
except AssertionError: # for BipedWalker BUG 2020-03-03
print(
"AssertionError: OpenAI gym r.LengthSquared() > 0.0f ??? Please run again."
)
return False
train_time = recorder.show_and_save(env_name, cwd)
# agent.save_or_load_model(cwd, is_save=True) # save max reward agent in Recorder
# memo.save_or_load_memo(cwd, is_save=True)
draw_plot_with_npy(cwd, train_time)
return True
def process__workers(gpu_id, root_cwd, q_aggr, q_dist, args, **_kwargs):
class_agent = args.class_agent
env_name = args.env_name
cwd = args.cwd
net_dim = args.net_dim
max_step = args.max_step
# max_memo = args.max_memo
max_epoch = args.max_epoch
batch_size = args.batch_size * 1.5
gamma = args.gamma
update_gap = args.update_gap
reward_scale = args.reward_scale
cwd = '{}/{}_{}'.format(root_cwd, cwd, gpu_id)
os.makedirs(cwd, exist_ok=True)
os.environ['CUDA_VISIBLE_DEVICES'] = str(gpu_id)
random_seed = 42 + gpu_id
np.random.seed(random_seed)
torch.manual_seed(random_seed)
torch.set_default_dtype(torch.float32)
torch.set_num_threads(8)
env = gym.make(env_name)
is_solved = False
class BufferArrayMP(BufferArray):
def init_before_sample(self):
q_aggr.put((self.memories, is_solved))
# self.now_len = self.max_len if self.is_full else self.next_idx
def random_sample(self, _batch_size, device=None):
batch_arrays = q_dist.get()
'''convert array into torch.tensor'''
tensors = [torch.tensor(ary, device=device) for ary in batch_arrays]
return tensors
'''init'''
state_dim, action_dim, max_action, target_reward, is_discrete = get_env_info(env, is_print=True)
agent = class_agent(env, state_dim, action_dim, net_dim) # training agent
buffer = BufferArrayMP(max_step, state_dim, action_dim) # experiment replay buffer
recorder = Recorder(agent, max_step, max_action, target_reward, env_name, **_kwargs)
'''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)
# recorder.show_reward(rewards, steps, 0, 0)
try:
for epoch in range(max_epoch):
'''update replay buffer by interact with environment'''
with torch.no_grad(): # for saving the GPU buffer
rewards, steps = agent.update_buffer(env, buffer, max_step, max_action, reward_scale, gamma)
'''update network parameters by random sampling buffer for stochastic gradient descent'''
loss_a, loss_c = agent.update_parameters(buffer, max_step, batch_size, update_gap)
'''show/check the reward, save the max reward actor'''
with torch.no_grad(): # for saving the GPU buffer
'''NOTICE! Recorder saves the agent with max reward automatically. '''
recorder.show_reward(rewards, steps, loss_a, loss_c)
is_solved = recorder.check_reward(cwd, loss_a, loss_c)
if is_solved:
break
except KeyboardInterrupt:
print("raise KeyboardInterrupt while training.")
# except AssertionError: # for BipedWalker BUG 2020-03-03
# print("AssertionError: OpenAI gym r.LengthSquared() > 0.0f ??? Please run again.")
# return False
train_time = recorder.print_and_save_npy(env_name, cwd)
# agent.save_or_load_model(cwd, is_save=True) # save max reward agent in Recorder
# buffer.save_or_load_memo(cwd, is_save=True)
draw_plot_with_npy(cwd, train_time)
return True
