env = gym.make(args.env_name)
num_inputs = env.observation_space.shape[0]
num_actions = env.action_space.shape[0]
env.seed(args.seed)
torch.manual_seed(args.seed)
if args.use_joint_pol_val:
ac_net = ActorCritic(num_inputs, num_actions)
opt_ac = optim.Adam(ac_net.parameters(), lr=0.0003)
else:
policy_net = GRU(num_inputs, num_actions)
old_policy_net = GRU(num_inputs, num_actions)
value_net = Value(num_inputs)
opt_policy = optim.Adam(policy_net.parameters(), lr=0.0003)
opt_value = optim.Adam(value_net.parameters(), lr=0.0003)
def create_batch_inputs(batch_states_list, batch_actions_list,
batch_advantages_list, batch_targets_list):
lengths = []
for states in batch_states_list:
lengths.append(states.size(0))
max_length = max(lengths)
batch_states = torch.zeros(len(batch_states_list), max_length, num_inputs)
batch_actions = torch.zeros(len(batch_actions_list), max_length,
num_actions)
batch_advantages = torch.zeros(len(batch_advantages_list), max_length)
batch_mask = []
test_set.append(data_set[t])
sequence_length = 401
input_size = 3
hidden_size = 16 # TODO: this parameter can be further tuned
num_layers = 1 # TODO: this parameter can be further tuned
batch_size = 1
num_epochs = 20 # 30 for 0.0001, and 20 for 0.0002
learning_rate = 0.0002
f = open("log_lr.txt", "a")
f.write("\nLength: {}, Step: {}\n".format(t_l[ktr1], step[ktr1][ktr2]))
rnn = GRU(input_size, hidden_size, num_layers, 1)
rnn.cuda()
criterion = nn.MSELoss()
optimizer = torch.optim.Adam(rnn.parameters(), lr=learning_rate)
losses = []
ktr_in = 0
for epoch in range(num_epochs):
for img, lb in train_set:
img = np.array([img,], dtype=np.float)
img = torch.FloatTensor(img)
img = Variable(img).cuda()
# Forward + Backward + Optimize
optimizer.zero_grad()
output = rnn(img)
crt = torch.FloatTensor(np.array([lb,], dtype=np.float))
crt = Variable(crt).cuda()
loss = criterion(output, crt)
loss.backward()
env.seed(args.seed)
torch.manual_seed(args.seed)
if args.use_joint_pol_val:
ac_net = ActorCritic(num_inputs, num_actions)
opt_ac = optim.Adam(ac_net.parameters(), lr=0.0003)
else:
policy_net = GRU(num_inputs, num_actions, dtype=dtype).type(dtype)
old_policy_net = GRU(num_inputs, num_actions, dtype=dtype).type(dtype)
value_net = Value(num_inputs).type(dtype)
reward_net = GRU(num_inputs + num_actions,
1,
policy_flag=0,
activation_flag=2,
dtype=dtype).type(dtype)
opt_policy = optim.Adam(policy_net.parameters(), lr=0.0003)
opt_value = optim.Adam(value_net.parameters(), lr=0.0003)
opt_reward = optim.Adam(reward_net.parameters(), lr=0.0003)
def create_batch_inputs(batch_states_list,
batch_actions_list,
batch_advantages_list=None):
lengths = []
for states in batch_states_list:
lengths.append(states.size(0))
max_length = max(lengths)
batch_states = torch.zeros(len(batch_states_list), max_length,
num_inputs).type(dtype)
batch_actions = torch.zeros(len(batch_actions_list), max_length,
test_set.append(all_discharge_data[i])
shapes = [(16, 1, (2, 3)), (32, 1, (1, 2, 3)), (64, 1, (1, 2, 3)),
(32, 2, (1, 2, 3)), (64, 2, (1, 2, 3))]
strategies = [(1, 0.0001, 40), (2, 0.00015, 30), (4, 0.0002, 24),
(8, 0.0003, 18)]
f = open("log_gru.txt", "a")
for shape in shapes:
for option in shape[2]:
strategy = strategies[option]
rnn = GRU(3, shape[0], shape[1], 1)
rnn.cuda()
criterion = nn.MSELoss()
optimizer = torch.optim.Adam(rnn.parameters(), lr=strategy[1])
for epoch in range(strategy[2]):
for i in range(int(len(train_set) / strategy[0])):
img = []
lb = []
for j in range(strategy[0]):
img.append(
np.array(train_set[i * strategy[0] + j].data,
dtype=np.float).transpose())
lb.append(
np.array(train_set[i * strategy[0] + j].SOH,
dtype=np.float))
img = np.array(img)
img = torch.FloatTensor(img)
img = Variable(img).cuda()
lb = np.array(lb)
