def main():
cuda0 = torch.device('cuda:0')
v_num = 32
# discount_gamma = 0.9
epsilon = 0
learning_rate = 0.0001
epsilon_cut = 0.99
epsilon_min = 0
#跟新轮次设定
iteration_num = 2
v_state_num = 2
batch_size = 100
start_time = datetime.datetime.now()
hv_env_num = 100
envs = hvenv('../data',v_state_num, v_num, hv_env_num,step_num,iteration_num)
hv_env_num_val= 1000
envs_val = hvenv('../data',v_state_num, v_num, hv_env_num_val,step_num,iteration_num)
rlnnet = DQN(2*v_num**2,2**step_num).to(cuda0)
rln_tgt_net = DQN(2*v_num**2,2**step_num).to(cuda0)
optimizer = torch.optim.Adam(params=rlnnet.parameters(), lr=learning_rate)
exp_buffer = collections.deque()
exp_buffer_val = collections.deque()
obs = envs.reset()
obs_val = envs_val.reset()
reward = envs.reward()
reward_val = envs_val.reward()
i = 0
# rlnnet = torch.load('../weight/16step_multi_nobn_big_8192_large_ddqn_classical_init_-3learning_rate'+str(step_num)+'step_'+str(iteration_num)+'iteration_best_model.pth')
# rlnnet = torch.load('../weight/16step_multi_big_8192_large_ddqn_classical_init_-3learning_rate'+str(step_num)+'step_'+str(iteration_num)+'iteration_best_model.pth')
# rlnnet = torch.load('../weight/16step_multi_big_large_ddqn_classical_init_-3learning_rate'+str(step_num)+'step_'+str(iteration_num)+'iteration_best_model.pth')
while True:
if i % SYC_NUM == 0:
print('----------------------------')
print('start_time: ' + str(start_time))
print('i/SYC_NUM: ' + str(i / SYC_NUM))
print('syc epsilon: ' + str(epsilon))
print('learning_rate: '+ str(learning_rate))
print('v_state_num: '+str(v_state_num))
print('v_num: '+str(v_num))
print('hv_env_num: '+str(hv_env_num))
print('batch_size: '+str(batch_size))
print('step_num: '+str(step_num))
print('iteration_num: '+str(iteration_num))
print('BUFFER_START_NUM: '+str(BUFFER_START_NUM))
print('BUFFER_length_NUM: '+str(len(exp_buffer)))
print('----------------------------')
epsilon *= epsilon_cut
# torch.save(rlnnet, '../weight/16step_multi_big_large_ddqn_classical_init_-3learning_rate'+str(step_num)+'step_'+str(iteration_num)+'iteration_best_model.pth')
# torch.save(rlnnet, '../weight/16step_multi_big_8192_large_ddqn_classical_init_-3learning_rate'+str(step_num)+'step_'+str(iteration_num)+'iteration_best_model.pth')
# torch.save(rlnnet,'../weight/16step_multi_nobn_big_8192_large_ddqn_classical_init_-3learning_rate'+str(step_num)+'step_'+str(iteration_num)+'iteration_best_model.pth')
rln_tgt_net.load_state_dict(rlnnet.state_dict())
if epsilon < epsilon_min:
epsilon = epsilon_min
i += 1
rlnnet.train()
#获取经验
exp_buffer, obs, reward = fresh_exp_buffer(exp_buffer, rlnnet, envs, obs, epsilon, cuda0,reward,BUFFER_START_NUM,BUFFER_MAX_NUM)
optimizer.zero_grad()
exp_batch_index = np.random.choice(np.arange(len(exp_buffer)), size=batch_size, replace=False)
batch = batch_sample(exp_batch_index,exp_buffer)
loss_t = calc_loss(batch, rlnnet, rln_tgt_net, device=cuda0)
loss_t.backward()
optimizer.step()
if i%100==0:
rlnnet.eval()
print('-------eval testing: epsilon = 0 -------')
for _ in range(int(v_num*iteration_num/step_num)):
exp_buffer_val, obs_val, reward_val = fresh_exp_buffer(exp_buffer_val, rlnnet, envs_val, obs_val, 0, cuda0, reward_val,
10000, 11000,True)
print('length buffer: ' + str(len(exp_buffer_val)))
print('-------eval end-------')
def main():
cuda0 = torch.device('cuda:0')
v_num = 16
# discount_gamma = 0.9
epsilon = 0
learning_rate = 0.0001
epsilon_cut = 0.99
epsilon_min = 0
#跟新轮次设定
iteration_num = 1
v_state_num = 2
batch_size = 100
start_time = datetime.datetime.now()
hv_env_num = 100
envs = hvenv('../data', v_state_num, v_num, hv_env_num, step_num,
iteration_num)
hv_env_num_val = 1000
envs_val = hvenv('../data', v_state_num, v_num, hv_env_num_val, step_num,
iteration_num)
rlnnet = DQN(2 * v_num**2, 2**step_num).to(cuda0)
rln_tgt_net = DQN(2 * v_num**2, 2**step_num).to(cuda0)
optimizer = torch.optim.Adam(params=rlnnet.parameters(), lr=learning_rate)
exp_buffer = collections.deque()
exp_buffer_val = collections.deque()
obs = envs.reset()
obs_val = envs_val.reset()
reward = envs.reward()
reward_val = envs_val.reward()
i = 0
reward_record = 0
# rlnnet = torch.load(SAVE_PATH)
while True:
if i % SYC_NUM == 0:
print('----------------------------')
print('start_time: ' + str(start_time))
print('i/SYC_NUM: ' + str(i / SYC_NUM))
print('syc epsilon: ' + str(epsilon))
print('learning_rate: ' + str(learning_rate))
print('v_state_num: ' + str(v_state_num))
print('v_num: ' + str(v_num))
print('hv_env_num: ' + str(hv_env_num))
print('batch_size: ' + str(batch_size))
print('step_num: ' + str(step_num))
print('iteration_num: ' + str(iteration_num))
print('BUFFER_START_NUM: ' + str(BUFFER_START_NUM))
print('BUFFER_length_NUM: ' + str(len(exp_buffer)))
print('----------------------------')
epsilon *= epsilon_cut
rln_tgt_net.load_state_dict(rlnnet.state_dict())
if epsilon < epsilon_min:
epsilon = epsilon_min
i += 1
rlnnet.train()
#获取经验
exp_buffer, obs, reward = fresh_exp_buffer(exp_buffer, rlnnet, envs,
obs, epsilon, cuda0, reward,
BUFFER_START_NUM,
BUFFER_MAX_NUM)
optimizer.zero_grad()
exp_batch_index = np.random.choice(np.arange(len(exp_buffer)),
size=batch_size,
replace=False)
batch = batch_sample(exp_batch_index, exp_buffer)
loss_t = calc_loss(batch, rlnnet, rln_tgt_net, device=cuda0)
loss_t.backward()
optimizer.step()
if i % 100 == 0:
obs_val, reward_val, reward_record = eval(rlnnet, envs_val,
obs_val, epsilon, cuda0,
reward_val,
reward_record)