def train_agent(rl_agent, net_dim, batch_size, repeat_times, gamma,
reward_scale, cwd, env_name, max_memo, max_step,
max_total_step, eval_times1, eval_times2, gpu_id, show_gap,
if_stop, **_kwargs): # 2020-06-01
env, state_dim, action_dim, max_action, target_reward, is_discrete = build_gym_env(
env_name, is_print=False)
'''init: agent, buffer, recorder'''
recorder = Recorder(eval_size1=eval_times1,
eval_size2=eval_times2) # todo eva_size1
agent = rl_agent(state_dim, action_dim, net_dim) # training agent
agent.state = env.reset()
is_online_policy = bool(
rl_agent.__name__ in
{'AgentPPO', 'AgentGAE', 'AgentInterGAE', 'AgentDiscreteGAE'})
if is_online_policy:
buffer = BufferTupleOnline(max_memo)
else:
buffer = BufferArray(max_memo, state_dim,
1 if is_discrete else action_dim)
with torch.no_grad(): # update replay buffer
rewards, steps = initial_exploration(env, buffer, max_step,
max_action, reward_scale,
gamma, action_dim)
recorder.update__record_explore(steps, rewards, loss_a=0, loss_c=0)
'''loop'''
if_train = True
while if_train:
'''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 gradient descent'''
buffer.init_before_sample()
loss_a, loss_c = agent.update_parameters(buffer, max_step, batch_size,
repeat_times)
# if loss_c > 4: # todo backtracking
# agent.save_or_load_model(cwd, if_save=False)
'''saves the agent with max reward'''
with torch.no_grad(): # for saving the GPU buffer
recorder.update__record_explore(steps, rewards, loss_a, loss_c)
if_save = recorder.update__record_evaluate(env, agent.act,
max_step, max_action,
agent.device,
is_discrete)
recorder.save_act(cwd, agent.act, gpu_id) if if_save else None
recorder.save_npy__plot_png(cwd)
if_solve = recorder.check_is_solved(target_reward, gpu_id,
show_gap)
'''break loop rules'''
if_train = not ((if_stop and if_solve)
or recorder.total_step > max_total_step
or os.path.exists(f'{cwd}/stop.mark'))
recorder.save_npy__plot_png(cwd)
def train_agent(rl_agent, env_name, gpu_id, cwd, net_dim, max_memo, max_step,
batch_size, repeat_times, reward_scale, gamma, break_step,
if_break_early, show_gap, eval_times1, eval_times2,
**_kwargs): # 2020-09-18
env, state_dim, action_dim, target_reward, if_discrete = build_gym_env(
env_name, if_print=False)
'''init: agent, buffer, recorder'''
recorder = Recorder(eval_size1=eval_times1, eval_size2=eval_times2)
agent = rl_agent(state_dim, action_dim, net_dim) # training agent
agent.state = env.reset()
if_online_policy = bool(
rl_agent.__name__ in
{'AgentPPO', 'AgentGAE', 'AgentInterGAE', 'AgentDiscreteGAE'})
if if_online_policy:
buffer = BufferTupleOnline(max_memo)
else:
buffer = BufferArray(max_memo, state_dim,
1 if if_discrete else action_dim)
with torch.no_grad(): # update replay buffer
rewards, steps = initial_exploration(env, buffer, max_step,
if_discrete, reward_scale,
gamma, action_dim)
recorder.update__record_explore(steps, rewards, loss_a=0, loss_c=0)
'''pre training and hard update before training loop'''
buffer.init_before_sample()
agent.update_parameters(buffer, max_step, batch_size, repeat_times)
agent.act_target.load_state_dict(agent.act.state_dict())
'''loop'''
if_train = True
while if_train:
'''update replay buffer by interact with environment'''
with torch.no_grad(): # speed up running
rewards, steps = agent.update_buffer(env, buffer, max_step,
reward_scale, gamma)
'''update network parameters by random sampling buffer for gradient descent'''
buffer.init_before_sample()
loss_a, loss_c = agent.update_parameters(buffer, max_step, batch_size,
repeat_times)
'''saves the agent with max reward'''
with torch.no_grad(): # for saving the GPU buffer
recorder.update__record_explore(steps, rewards, loss_a, loss_c)
if_save = recorder.update__record_evaluate(env, agent.act,
max_step, agent.device,
if_discrete)
recorder.save_act(cwd, agent.act, gpu_id) if if_save else None
recorder.save_npy__plot_png(cwd)
if_solve = recorder.check_is_solved(target_reward, gpu_id,
show_gap)
'''break loop rules'''
if_train = not ((if_break_early and if_solve)
or recorder.total_step > break_step
or os.path.exists(f'{cwd}/stop'))
recorder.save_npy__plot_png(cwd)
buffer.print_state_norm(env.neg_state_avg, env.div_state_std)
def mp__update_params(
args, q_i_eva,
q_o_eva): # 2020-11-11 update network parameters using replay buffer
rl_agent = args.rl_agent
max_memo = args.max_memo
net_dim = args.net_dim
max_step = args.max_step
max_total_step = args.break_step
batch_size = args.batch_size
repeat_times = args.repeat_times
cwd = args.cwd
env_name = args.env_name
reward_scale = args.reward_scale
if_stop = args.if_break_early
gamma = args.gamma
del args
env, state_dim, action_dim, target_reward, if_discrete = build_env(
env_name, if_print=False)
'''build agent'''
agent = rl_agent(state_dim, action_dim, net_dim) # training agent
agent.state = env.reset()
'''send agent to q_i_eva'''
from copy import deepcopy
act_cpu = deepcopy(agent.act).to(torch.device("cpu"))
act_cpu.eval()
[setattr(param, 'requires_grad', False) for param in act_cpu.parameters()]
q_i_eva.put(act_cpu) # q_i_eva 1.
'''build replay buffer, init: total_step, reward_avg'''
total_step = 0
if bool(rl_agent.__name__ in {
'AgentPPO',
}):
buffer = BufferTupleOnline(max_memo)
with torch.no_grad():
reward_avg = get_total_return(env, act_cpu, max_step,
torch.device("cpu"), if_discrete)
elif bool(rl_agent.__name__ in {'AgentModPPO', 'AgentInterPPO'}):
buffer = BufferArrayGPU(max_memo + max_step,
state_dim,
action_dim,
if_ppo=True) # experiment replay buffer
with torch.no_grad():
reward_avg = get_total_return(env, act_cpu, max_step,
torch.device("cpu"), if_discrete)
else:
buffer = BufferArrayGPU(max_memo,
state_dim,
action_dim=1 if if_discrete else action_dim,
if_ppo=False)
'''initial exploration'''
with torch.no_grad(): # update replay buffer
rewards, steps = initial_exploration(env, buffer, max_step,
if_discrete, reward_scale,
gamma, action_dim)
reward_avg = np.average(rewards)
step_sum = sum(steps)
'''pre training and hard update before training loop'''
buffer.update_pointer_before_sample()
agent.update_policy(buffer, max_step, batch_size, repeat_times)
if 'act_target' in dir(agent):
agent.act_target.load_state_dict(agent.act.state_dict())
q_i_eva.put((act_cpu, reward_avg, step_sum, 0, 0)) # q_i_eva n.
total_step += step_sum
'''training loop'''
if_train = True
if_solve = False
while if_train:
'''update replay buffer by interact with environment'''
with torch.no_grad(): # speed up running
rewards, steps = agent.update_buffer(env, buffer, max_step,
reward_scale, gamma)
reward_avg = np.average(rewards) if len(rewards) else reward_avg
step_sum = sum(steps)
total_step += step_sum
'''update network parameters by random sampling buffer for gradient descent'''
buffer.update_pointer_before_sample()
loss_a_avg, loss_c_avg = agent.update_policy(buffer, max_step,
batch_size, repeat_times)
'''saves the agent with max reward'''
act_cpu.load_state_dict(agent.act.state_dict())
q_i_eva.put((act_cpu, reward_avg, step_sum, loss_a_avg,
loss_c_avg)) # q_i_eva n.
if q_o_eva.qsize() > 0:
if_solve = q_o_eva.get() # q_o_eva n.
'''break loop rules'''
if_train = not ((if_stop and if_solve) or total_step > max_total_step
or os.path.exists(f'{cwd}/stop'))
env, state_dim, action_dim, target_reward, if_discrete = build_env(
env_name, if_print=False)
buffer.print_state_norm(env.neg_state_avg, env.div_state_std)
q_i_eva.put('stop')
while q_i_eva.qsize() > 0:
time.sleep(1)
time.sleep(4)