def build(self):
self.policy_net1 = DQN2D(84, 84, self.pars).to(self.device)
self.target_net1 = DQN2D(84, 84, self.pars).to(self.device)
self.target_net1.load_state_dict(self.policy_net1.state_dict())
self.target_net1.eval()
self.policy_net2 = DQN2D(84, 84, self.pars).to(self.device)
self.target_net2 = DQN2D(84, 84, self.pars).to(self.device)
self.target_net2.load_state_dict(self.policy_net2.state_dict())
self.target_net2.eval()
self.optimizer1 = optim.SGD(self.policy_net1.parameters(),
lr=self.pars['lr'],
momentum=self.pars['momentum']) #
self.optimizer2 = optim.SGD(self.policy_net2.parameters(),
lr=self.pars['lr'],
momentum=self.pars['momentum']) #
#self.optimizer1 = optim.Adam(self.policy_net1.parameters())
#self.optimizer2 = optim.Adam(self.policy_net2.parameters())
self.memory2 = ReplayMemory(10000)
self.memory1 = ReplayMemory(10000)
if self.pars['ppe'] == '1':
self.memory1 = Memory(10000)
self.memory2 = Memory(10000)
def __init__(self, env, exploration_method='epsilon_greedy'):
self.action_space = env.action_space
self.buffer = ReplayMemory(BUFFER_SIZE, MINI_BATCH_SIZE)
self.exploration_method = exploration_method
self.brain = MLP(env.observation_space.shape[0], env.action_space.n)
self.brain_bis = copy.deepcopy(self.brain)
self.Tensor = torch.Tensor
self.LongTensor = torch.LongTensor
self.cpt = 0
self.optimizer = optim.Adam(self.brain.parameters())
def build(self):
self.policy_net = DQN(71, self.pars).to(self.device)
self.q_net = DQN(71, self.pars).to(self.device)
self.target_net = DQN(71, self.pars).to(self.device)
self.target_net.load_state_dict(self.q_net.state_dict())
self.target_net.eval()
if self.pars['momentum']>0:
self.optimizer = optim.SGD(
self.q_net.parameters(), lr=self.pars['lr'],
momentum=self.pars['momentum'])#
self.policy_optimizer = optim.SGD(
self.policy_net.parameters(), lr=self.pars['lr'],
momentum=self.pars['momentum'])#
else:
self.optimizer = optim.Adam(self.q_net.parameters())
self.policy_optimizer = optim.Adam(self.policy_net.parameters())
self.memory = ReplayMemory(10000)
self.eps_threshold = 0.01
self.bufs = [[] for _ in range(len(self.envs)*2)]
def __init__(self, env, args, work_dir):
self.env = env
self.args = args
self.work_dir = work_dir
self.n_action = self.env.action_space.n
self.arr_actions = np.arange(self.n_action)
self.memory = ReplayMemory(self.args.buffer_size, self.args.device)
self.qNetwork = ValueNetwork(self.n_action,
self.env).to(self.args.device)
self.targetNetwork = ValueNetwork(self.n_action,
self.env).to(self.args.device)
self.qNetwork.train()
self.targetNetwork.eval()
self.optimizer = optim.RMSprop(self.qNetwork.parameters(),
lr=0.00025,
eps=0.001,
alpha=0.95)
self.crit = nn.MSELoss()
self.eps = max(self.args.eps, self.args.eps_min)
self.eps_delta = (
self.eps - self.args.eps_min) / self.args.exploration_decay_speed
def __init__(self,
env,
lr=3e-4,
gamma=0.99,
polyak=5e-3,
alpha=0.2,
reward_scale=1.0,
cuda=True,
writer=None):
state_size = env.observation_space.shape[0]
action_size = env.action_space.shape[0]
self.actor = Actor(state_size, action_size)
self.critic = Critic(state_size, action_size)
self.target_critic = Critic(state_size, action_size).eval()
self.actor_optimizer = optim.Adam(self.actor.parameters(), lr=lr)
self.q1_optimizer = optim.Adam(self.critic.q1.parameters(), lr=lr)
self.q2_optimizer = optim.Adam(self.critic.q2.parameters(), lr=lr)
self.target_critic.load_state_dict(self.critic.state_dict())
for param in self.target_critic.parameters():
param.requires_grad = False
self.memory = ReplayMemory()
self.gamma = gamma
self.alpha = alpha
self.polyak = polyak # Always between 0 and 1, usually close to 1
self.reward_scale = reward_scale
self.writer = writer
self.cuda = cuda
if cuda:
self.actor = self.actor.to('cuda')
self.critic = self.critic.to('cuda')
self.target_critic = self.target_critic.to('cuda')
def build(self):
self.policy_net = DQN(97, self.pars,
rec=self.pars['rec'] == 1).to(self.device)
self.target_net = DQN(97, self.pars,
rec=self.pars['rec'] == 1).to(self.device)
self.target_net.load_state_dict(self.policy_net.state_dict())
self.target_net.eval()
if self.pars['momentum'] > 0:
self.optimizer = optim.SGD(self.policy_net.parameters(),
lr=self.pars['lr'],
momentum=self.pars['momentum']) #
else:
self.optimizer = optim.Adam(self.policy_net.parameters())
self.memory = ReplayMemory(10000)
if 'ppe' in self.pars:
self.memory = Memory(10000)
if self.pars['load'] is not None:
self.load(self.pars['load'])
self.target_net.load_state_dict(self.policy_net.state_dict())
print('loaded')
def build(self):
self.policy_net = DQN2D(84,84, self.pars, rec=self.pars['rec']==1).to(self.device)
self.q_net = DQN2D(84,84, self.pars).to(self.device)
self.target_net = DQN2D(84,84, self.pars).to(self.device)
self.target_net.load_state_dict(self.q_net.state_dict())
self.target_net.eval()
if self.pars['momentum']>0:
self.optimizer = optim.SGD(
self.q_net.parameters(), lr=self.pars['lr'],
momentum=self.pars['momentum'])#
self.policy_optimizer = optim.SGD(
self.policy_net.parameters(), lr=self.pars['lr'],
momentum=self.pars['momentum'])#
else:
self.optimizer = optim.Adam(self.q_net.parameters())
self.policy_optimizer = optim.Adam(self.policy_net.parameters())
self.memory = ReplayMemory(10000)
if self.pars['ppe'] == '1':
self.memory = Memory(10000)
self.eps_threshold = 0.01
class DQNAgent(object):
def __init__(self, env, args, work_dir):
self.env = env
self.args = args
self.work_dir = work_dir
self.n_action = self.env.action_space.n
self.arr_actions = np.arange(self.n_action)
self.memory = ReplayMemory(self.args.buffer_size, self.args.device)
self.qNetwork = ValueNetwork(self.n_action,
self.env).to(self.args.device)
self.targetNetwork = ValueNetwork(self.n_action,
self.env).to(self.args.device)
self.qNetwork.train()
self.targetNetwork.eval()
self.optimizer = optim.RMSprop(self.qNetwork.parameters(),
lr=0.00025,
eps=0.001,
alpha=0.95)
self.crit = nn.MSELoss()
self.eps = max(self.args.eps, self.args.eps_min)
self.eps_delta = (
self.eps - self.args.eps_min) / self.args.exploration_decay_speed
def reset(self):
return torch.cat([preprocess_state(self.env.reset(), self.env)] * 4, 1)
def select_action(self, state):
action_prob = np.zeros(self.n_action, np.float32)
action_prob.fill(self.eps / self.n_action)
max_q, max_q_index = self.qNetwork(Variable(state.to(
self.args.device))).data.cpu().max(1)
action_prob[max_q_index[0]] += 1 - self.eps
action = np.random.choice(self.arr_actions, p=action_prob)
next_state, reward, done, _ = self.env.step(action)
next_state = torch.cat(
[state.narrow(1, 1, 3),
preprocess_state(next_state, self.env)], 1)
self.memory.push(
(state, torch.LongTensor([int(action)]), torch.Tensor([reward]),
next_state, torch.Tensor([done])))
return next_state, reward, done, max_q[0]
def run(self):
state = self.reset()
# init buffer
for _ in range(self.args.buffer_init_size):
next_state, _, done, _ = self.select_action(state)
state = self.reset() if done else next_state
total_frame = 0
reward_list = np.zeros(self.args.log_size, np.float32)
qval_list = np.zeros(self.args.log_size, np.float32)
start_time = time.time()
for epi in count():
reward_list[epi % self.args.log_size] = 0
qval_list[epi % self.args.log_size] = -1e9
state = self.reset()
done = False
ep_len = 0
if epi % self.args.save_freq == 0:
model_file = os.path.join(self.work_dir, 'model.th')
with open(model_file, 'wb') as f:
torch.save(self.qNetwork, f)
while not done:
if total_frame % self.args.sync_period == 0:
self.targetNetwork.load_state_dict(
self.qNetwork.state_dict())
self.eps = max(self.args.eps_min, self.eps - self.eps_delta)
next_state, reward, done, qval = self.select_action(state)
reward_list[epi % self.args.log_size] += reward
qval_list[epi % self.args.log_size] = max(
qval_list[epi % self.args.log_size], qval)
state = next_state
total_frame += 1
ep_len += 1
if ep_len % self.args.learn_freq == 0:
batch_state, batch_action, batch_reward, batch_next_state, batch_done = self.memory.sample(
self.args.batch_size)
batch_q = self.qNetwork(batch_state).gather(
1, batch_action.unsqueeze(1)).squeeze(1)
batch_next_q = self.targetNetwork(batch_next_state).detach(
).max(1)[0] * self.args.gamma * (1 - batch_done)
loss = self.crit(batch_q, batch_reward + batch_next_q)
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
output_str = 'episode %d frame %d time %.2fs cur_rew %.3f mean_rew %.3f cur_maxq %.3f mean_maxq %.3f' % (
epi, total_frame, time.time() - start_time,
reward_list[epi % self.args.log_size], np.mean(reward_list),
qval_list[epi % self.args.log_size], np.mean(qval_list))
print(output_str)
logging.info(output_str)
class AgentSep1D(Agent):
def __init__(self, name, pars, nrenvs=1, job=None, experiment=None):
Agent.__init__(self, name, pars, nrenvs, job, experiment)
def build(self):
self.policy_net1 = DQN(71, self.pars).to(self.device)
self.target_net1 = DQN(71, self.pars).to(self.device)
self.target_net1.load_state_dict(self.policy_net1.state_dict())
self.target_net1.eval()
self.policy_net2 = DQN(71, self.pars).to(self.device)
self.target_net2 = DQN(71, self.pars).to(self.device)
self.target_net2.load_state_dict(self.policy_net2.state_dict())
self.target_net2.eval()
self.optimizer1 = optim.SGD(self.policy_net1.parameters(),
lr=self.pars['lr'],
momentum=self.pars['momentum']) #
self.optimizer2 = optim.SGD(self.policy_net2.parameters(),
lr=self.pars['lr'],
momentum=self.pars['momentum']) #
self.optimizer1 = optim.Adam(self.policy_net1.parameters())
self.optimizer2 = optim.Adam(self.policy_net2.parameters())
self.memory2 = ReplayMemory(10000)
self.memory1 = ReplayMemory(10000)
def getaction(self, state1, state2, test=False):
mes = torch.tensor([[0, 0, 0, 0]], device=self.device)
comm2 = self.policy_net1(
state2, 0,
mes)[self.idC].detach() if np.random.rand() < self.prob else mes
comm1 = self.policy_net2(
state1, 0,
mes)[self.idC].detach() if np.random.rand() < self.prob else mes
if test:
action1 = self.policy_net1(state1, 1,
comm2)[0].max(1)[1].view(1, 1)
action2 = self.policy_net2(state2, 1,
comm1)[0].max(1)[1].view(1, 1)
else:
action1 = self.select_action(state1, comm2, self.policy_net1)
action2 = self.select_action(state2, comm1, self.policy_net2)
return action1, action2, [comm1, comm2]
def getStates(self, env):
screen1 = env.render_env_1d() #.transpose((2, 0, 1))
return torch.from_numpy(screen1).unsqueeze(0).to(
self.device), torch.from_numpy(screen1).unsqueeze(0).to(
self.device)
def saveStates(self, state1, state2, action1, action2, next_state1,
next_state2, reward1, reward2, env_id):
self.capmem += 2
if self.pars['ppe'] != '1':
self.memory2.push(state2, action2, next_state2, reward2, state1)
self.memory1.push(state1, action1, next_state1, reward1, state2)
else:
self.memory1.store([state1, action1, next_state1, reward1, state2])
self.memory2.store([state2, action2, next_state2, reward2, state1])
#self.memory2.push(state2, action2, next_state2, reward2, state1)
#self.memory1.push(state1, action1, next_state1, reward1, state2)
def optimize(self):
self.optimize_model(self.policy_net1, self.target_net1, self.memory1,
self.optimizer1)
self.optimize_model(self.policy_net2, self.target_net2, self.memory2,
self.optimizer2)
def updateTarget(self, i_episode, step=False):
#soft_update(self.target_net, self.policy_net, tau=0.01)
if step:
return
if i_episode % self.TARGET_UPDATE == 0:
self.target_net1.load_state_dict(self.policy_net1.state_dict())
self.target_net2.load_state_dict(self.policy_net2.state_dict())
def save(self):
torch.save(self.policy_net1.state_dict(),
self.pars['results_path'] + self.name + '/model1')
torch.save(self.policy_net2.state_dict(),
self.pars['results_path'] + self.name + '/model2')
def perturb_learning_rate(self, i_episode, nolast=True):
if nolast:
new_lr_factor = 10**np.random.normal(scale=1.0)
new_momentum_delta = np.random.normal(scale=0.1)
self.EPS_DECAY += np.random.normal(scale=50.0)
if self.EPS_DECAY < 50:
self.EPS_DECAY = 50
if self.prob >= 0:
self.prob += np.random.normal(scale=0.05) - 0.025
self.prob = min(max(0, self.prob), 1)
for param_group in self.optimizer1.param_groups:
if nolast:
param_group['lr'] *= new_lr_factor
param_group['momentum'] += new_momentum_delta
self.momentum1 = param_group['momentum']
self.lr1 = param_group['lr']
if nolast:
new_lr_factor = 10**np.random.normal(scale=1.0)
new_momentum_delta = np.random.normal(scale=0.1)
for param_group in self.optimizer2.param_groups:
if nolast:
param_group['lr'] *= new_lr_factor
param_group['momentum'] += new_momentum_delta
self.momentum2 = param_group['momentum']
self.lr2 = param_group['lr']
with open(
os.path.join(self.pars['results_path'] + self.name,
'hyper-{}.json').format(i_episode),
'w') as outfile:
json.dump(
{
'lr1': self.lr1,
'momentum1': self.momentum1,
'lr2': self.lr2,
'momentum2': self.momentum2,
'eps_decay': self.EPS_DECAY,
'prob': self.prob,
'i_episode': i_episode
}, outfile)
def clone(self, agent):
state_dict = agent.policy_net1.state_dict()
self.policy_net1.load_state_dict(state_dict)
state_dict = agent.optimizer1.state_dict()
self.optimizer1.load_state_dict(state_dict)
state_dict = agent.policy_net2.state_dict()
self.policy_net2.load_state_dict(state_dict)
state_dict = agent.optimizer2.state_dict()
self.optimizer2.load_state_dict(state_dict)
self.target_net1.load_state_dict(self.policy_net1.state_dict())
self.target_net2.load_state_dict(self.policy_net2.state_dict())
self.EPS_DECAY = agent.EPS_DECAY
class Agent(object):
def __init__(self, env, exploration_method='epsilon_greedy'):
self.action_space = env.action_space
self.buffer = ReplayMemory(BUFFER_SIZE, MINI_BATCH_SIZE)
self.exploration_method = exploration_method
self.brain = MLP(env.observation_space.shape[0], env.action_space.n)
self.brain_bis = copy.deepcopy(self.brain)
self.Tensor = torch.Tensor
self.LongTensor = torch.LongTensor
self.cpt = 0
self.optimizer = optim.Adam(self.brain.parameters())
def act(self, ob, reward, done):
if self.exploration_method == 'epsilon_greedy':
if random.random() < EPSILON:
return self.action_space.sample()
else:
return self.get_best_action(ob)
def learn(self, prev_ob, action, ob, reward, done):
self.cpt += 1
if self.cpt % 10 == 0:
self.brain_bis = copy.deepcopy(self.brain)
self.buffer.add(prev_ob, action, ob, reward, done)
batch = self.buffer.get_minibatch()
[states, actions, next_states, rewards, dones] = zip(*batch)
state_batch = Variable(self.Tensor(states))
action_batch = Variable(self.LongTensor(actions))
reward_batch = Variable(self.Tensor(rewards))
next_states_batch = Variable(self.Tensor(next_states))
state_action_values = self.brain(state_batch).gather(1, action_batch.view(-1,1)).view(-1)
with torch.no_grad():
next_state_values = self.brain_bis(next_states_batch).max(1)[0]
for i in range(len(batch)):
if dones[i]:
next_state_values.data[i]=0
# Compute the expected Q values
expected_state_action_values = (next_state_values * GAMMA) + reward_batch
loss = F.mse_loss(state_action_values, expected_state_action_values)
self.optimizer.zero_grad()
loss.backward()
nn.utils.clip_grad_norm(self.brain.parameters(), 10) # Clip gradients (normalising by max value of gradient L2 norm)
self.optimizer.step()
'''
for interaction in minibatch:
self.cpt += 1
if self.cpt % 100:
self.brain_bis = copy.deepcopy(self.brain)
q_vals_pred = self.brain(torch.tensor(interaction[0]).float())
q_vals_pred_next = self.brain_bis(torch.tensor(interaction[2]).float())
q_vals = [None for _ in range(self.action_space.n)]
if interaction[4]:
for i in range(self.action_space.n):
q_vals[i] = (q_vals_pred[i] - interaction[3]) ** 2
else:
for i in range(self.action_space.n):
q_vals[i] = (q_vals_pred[i] - (interaction[3] + GAMMA * torch.max(q_vals_pred_next).item())) ** 2
self.brain.train()
loss = self.brain.loss(q_vals_pred, torch.tensor(q_vals).float())
loss = Variable(loss, requires_grad=True)
loss.backward()
'''
def get_q_val(self, ob, action):
return self.get_q_vals(ob)[action]
def get_best_action(self, ob):
index = torch.argmax(self.get_q_vals(ob), 0)
print(index)
return index.int().item()
def get_max_q_val(self, ob):
val, _ = torch.max(self.get_q_vals(ob), 0)
return val.float().item()
def get_q_vals(self, ob):
self.brain.eval()
with torch.no_grad():
ob = torch.tensor(ob).float().unsqueeze(0)
print(ob.shape)
output = self.brain(ob)
print(output.shape)
return output
class AgentACShare1D(Agent):
def __init__(self, name, pars, nrenvs=1, job=None, experiment=None):
Agent.__init__(self,name, pars, nrenvs, job, experiment)
def build(self):
self.policy_net = DQN(71, self.pars).to(self.device)
self.q_net = DQN(71, self.pars).to(self.device)
self.target_net = DQN(71, self.pars).to(self.device)
self.target_net.load_state_dict(self.q_net.state_dict())
self.target_net.eval()
if self.pars['momentum']>0:
self.optimizer = optim.SGD(
self.q_net.parameters(), lr=self.pars['lr'],
momentum=self.pars['momentum'])#
self.policy_optimizer = optim.SGD(
self.policy_net.parameters(), lr=self.pars['lr'],
momentum=self.pars['momentum'])#
else:
self.optimizer = optim.Adam(self.q_net.parameters())
self.policy_optimizer = optim.Adam(self.policy_net.parameters())
self.memory = ReplayMemory(10000)
self.eps_threshold = 0.01
self.bufs = [[] for _ in range(len(self.envs)*2)]
def updateTarget(self, i_episode, step=False):
#soft_update(self.target_net, self.policy_net, tau=0.01)
if step:
return
self.optimize_policy(self.policy_net, self.bufs, self.policy_optimizer)
if i_episode % self.TARGET_UPDATE == 0:
self.target_net.load_state_dict(self.q_net.state_dict())
self.eps_threshold -= 0.001
def saveStates(self, state1, state2, action1,action2, next_state1,next_state2, reward1,reward2, env_id):
logp1, ent1, logp2, ent2 = self.rem
if self.pars['ppe']!='1':
self.memory.push(state2, action2, next_state2, reward2, state1)
self.memory.push(state1, action1, next_state1, reward1, state2)
else:
self.memory.store([state1, action1, next_state1, reward1, state2])
self.memory.store([state2, action2, next_state2, reward2, state1])
#self.buf2.append([state2, action2,1, reward2, logp2, ent2])
#self.buf1.append([state1, action1,1, reward1, logp1, ent1])
self.bufs[2*env_id ].append([state2, action2,1, reward2, logp2, ent2])
self.bufs[2*env_id+1].append([state1, action1,1, reward1, logp1, ent1])
def select_action(self, state, comm, policy_net):
probs1, _ = policy_net(state, 1, comm)#.cpu().data.numpy()
m = Categorical(logits=probs1)
action = m.sample()
return action.view(1, 1), m.log_prob(action), m.entropy()
def getComm(self, mes, policy_net, state1_batch):
return self.policy_net(state1_batch, 1, mes)[self.idC].detach() if np.random.rand()<self.prob else mes
def getaction(self, state1, state2, test=False):
mes = torch.tensor([[0,0,0,0]], device=self.device)
#maybe error
comm2 = self.policy_net(state2, 0, mes)[self.idC] if (test and 0<self.prob) or np.random.rand()<self.prob else mes
comm1 = self.policy_net(state1, 0, mes)[self.idC] if (test and 0<self.prob) or np.random.rand()<self.prob else mes
action1, logp1, ent1 = self.select_action(state1, comm2, self.policy_net)
action2, logp2, ent2 = self.select_action(state2, comm1, self.policy_net)
self.rem =[logp1, ent1, logp2, ent2]
return action1, action2, [comm1, comm2]
def optimize_policy(self, policy_net, memories, optimizer):
policy_loss = 0
value_loss = 0
ent = 0
for memory in memories:#[memory1, memory2]:
R = torch.zeros(1, 1, device=self.device)
#GAE = torch.zeros(1, 1, device=self.device)
saved_r = torch.cat([c[3].float() for c in memory])
states = torch.cat([c[0].float() for c in memory])
action_batch = torch.cat([c[1].float() for c in memory]).view(-1,1)
mes = torch.tensor([[0,0,0,0] for i in memory], device=self.device)
actionV = self.q_net(states, 0, mes)[0].gather(1, action_batch.long())
mu = saved_r.mean()
std = saved_r.std()
eps = 0.000001
#print(memory)
for i in reversed(range(len(memory)-1)):
_,_,_,r,log_prob, entr = memory[i]
ac = (actionV[i] - mu) / (std + eps)#actionV[i]#also use mu and std
#Discounted Sum of Future Rewards + reward for the given state
R = self.GAMMA * R + (r.float() - mu) / (std + eps)
advantage = R - ac
policy_loss += -log_prob *advantage .detach()
#ent += entr#*0
optimizer.zero_grad()
(policy_loss.mean() + self.eps_threshold*ent).backward()
for param in policy_net.parameters():
if param.grad is not None:
param.grad.data.clamp_(-1, 1)
optimizer.step()
def save(self):
torch.save(self.policy_net.state_dict(), self.pars['results_path']+self.name+'/model')
torch.save(self.q_net.state_dict(), self.pars['results_path']+self.name+'/modelQ')
def load(self, PATH):
#torch.cuda.is_available()
self.policy_net.load_state_dict(torch.load(PATH, map_location= 'cuda' if torch.cuda.is_available() else 'cpu'))
self.q_net.load_state_dict(torch.load(PATH+'Q', map_location= 'cuda' if torch.cuda.is_available() else 'cpu'))
self.target_net.load_state_dict(self.q_net.state_dict())
def optimize(self):
self.optimize_model(self.q_net, self.target_net, self.memory, self.optimizer)
def perturb_learning_rate(self, i_episode, nolast=True):
if nolast:
new_lr_factor = 10**np.random.normal(scale=1.0)
new_momentum_delta = np.random.normal(scale=0.1)
self.eps_threshold += np.random.normal(scale=0.1)
self.alpha += np.random.normal(scale=0.1)
if self.alpha>1:
self.alpha = 1
if self.alpha<0.5:
self.alpha = 0.5
if self.eps_threshold<0:
self.eps_threshold = 0.00001
self.EPS_DECAY += np.random.normal(scale=50.0)
if self.EPS_DECAY<50:
self.EPS_DECAY = 50
if self.prob>=0:
self.prob += np.random.normal(scale=0.05)-0.025
self.prob = min(max(0,self.prob),1)
for param_group in self.optimizer.param_groups:
if nolast:
param_group['lr'] *= new_lr_factor
param_group['momentum'] += new_momentum_delta
self.momentum =param_group['momentum']
self.lr = param_group['lr']
if nolast:
new_lr_factor = 10**np.random.normal(scale=1.0)
new_momentum_delta = np.random.normal(scale=0.1)
for param_group in self.policy_optimizer.param_groups:
if nolast:
param_group['lr'] *= new_lr_factor
param_group['momentum'] += new_momentum_delta
self.momentum1 =param_group['momentum']
self.lr1 = param_group['lr']
with open(os.path.join(self.pars['results_path']+ self.name,'hyper-{}.json').format(i_episode), 'w') as outfile:
json.dump({'lr':self.lr, 'momentum':self.momentum, 'alpha':self.alpha,
'lr1':self.lr1, 'momentum1':self.momentum1,'eps_decay':self.EPS_DECAY,
'eps_entropy':self.eps_threshold,
'prob':self.prob,'i_episode':i_episode}, outfile)
def clone(self, agent):
state_dict = agent.policy_net.state_dict()
self.policy_net.load_state_dict(state_dict)
state_dict = agent.policy_optimizer.state_dict()
self.policy_optimizer.load_state_dict(state_dict)
self.alpha = agent.alpha
state_dict = agent.q_net.state_dict()
self.q_net.load_state_dict(state_dict)
state_dict = agent.optimizer.state_dict()
self.optimizer.load_state_dict(state_dict)
self.target_net.load_state_dict(self.q_net.state_dict())
self.EPS_DECAY = agent.EPS_DECAY
self.eps_threshold = agent.eps_threshold
self.prob = agent.prob
class SAC:
def __init__(self,
env,
lr=3e-4,
gamma=0.99,
polyak=5e-3,
alpha=0.2,
reward_scale=1.0,
cuda=True,
writer=None):
state_size = env.observation_space.shape[0]
action_size = env.action_space.shape[0]
self.actor = Actor(state_size, action_size)
self.critic = Critic(state_size, action_size)
self.target_critic = Critic(state_size, action_size).eval()
self.actor_optimizer = optim.Adam(self.actor.parameters(), lr=lr)
self.q1_optimizer = optim.Adam(self.critic.q1.parameters(), lr=lr)
self.q2_optimizer = optim.Adam(self.critic.q2.parameters(), lr=lr)
self.target_critic.load_state_dict(self.critic.state_dict())
for param in self.target_critic.parameters():
param.requires_grad = False
self.memory = ReplayMemory()
self.gamma = gamma
self.alpha = alpha
self.polyak = polyak # Always between 0 and 1, usually close to 1
self.reward_scale = reward_scale
self.writer = writer
self.cuda = cuda
if cuda:
self.actor = self.actor.to('cuda')
self.critic = self.critic.to('cuda')
self.target_critic = self.target_critic.to('cuda')
def explore(self, state):
if self.cuda:
state = torch.tensor(state).unsqueeze(0).to('cuda', torch.float)
action, _, _ = self.actor.sample(state)
# action, _ = self.actor(state)
return action.cpu().detach().numpy().reshape(-1)
def exploit(self, state):
if self.cuda:
state = torch.tensor(state).unsqueeze(0).to('cuda', torch.float)
_, _, action = self.actor.sample(state)
return action.cpu().detach().numpy().reshape(-1)
def store_step(self, state, action, next_state, reward, terminal):
state = to_tensor_unsqueeze(state)
if action.dtype == np.float32:
action = torch.from_numpy(action)
next_state = to_tensor_unsqueeze(next_state)
reward = torch.from_numpy(np.array([reward]).astype(np.float))
terminal = torch.from_numpy(np.array([terminal]).astype(np.uint8))
self.memory.push(state, action, next_state, reward, terminal)
def target_update(self, target_net, net):
for t, s in zip(target_net.parameters(), net.parameters()):
# t.data.copy_(t.data * (1.0 - self.polyak) + s.data * self.polyak)
t.data.mul_(1.0 - self.polyak)
t.data.add_(self.polyak * s.data)
def calc_target_q(self, next_states, rewards, terminals):
with torch.no_grad():
next_action, entropy, _ = self.actor.sample(
next_states) # penalty term
next_q1, next_q2 = self.target_critic(next_states, next_action)
next_q = torch.min(next_q1, next_q2) - self.alpha * entropy
target_q = rewards * self.reward_scale + (
1. - terminals) * self.gamma * next_q
return target_q
def calc_critic_loss(self, states, actions, next_states, rewards,
terminals):
q1, q2 = self.critic(states, actions)
target_q = self.calc_target_q(next_states, rewards, terminals)
q1_loss = torch.mean((q1 - target_q).pow(2))
q2_loss = torch.mean((q2 - target_q).pow(2))
return q1_loss, q2_loss
def calc_actor_loss(self, states):
action, entropy, _ = self.actor.sample(states)
q1, q2 = self.critic(states, action)
q = torch.min(q1, q2)
# actor_loss = torch.mean(-q - self.alpha * entropy)
actor_loss = (self.alpha * entropy - q).mean()
return actor_loss, entropy
def train(self, timestep, batch_size=256):
if len(self.memory) < batch_size:
return
transitions = self.memory.sample(batch_size)
transitions = Transition(*zip(*transitions))
if self.cuda:
states = torch.cat(transitions.state).to('cuda')
actions = torch.stack(transitions.action).to('cuda')
next_states = torch.cat(transitions.next_state).to('cuda')
rewards = torch.stack(transitions.reward).to('cuda')
terminals = torch.stack(transitions.terminal).to('cuda')
else:
states = torch.cat(transitions.state)
actions = torch.stack(transitions.action)
next_states = torch.cat(transitions.next_state)
rewards = torch.stack(transitions.reward)
terminals = torch.stack(transitions.terminal)
# Compute target Q func
q1_loss, q2_loss = self.calc_critic_loss(states, actions, next_states,
rewards, terminals)
# Compute actor loss
actor_loss, mean = self.calc_actor_loss(states)
update_params(self.q1_optimizer, self.critic.q1, q1_loss)
update_params(self.q2_optimizer, self.critic.q2, q2_loss)
update_params(self.actor_optimizer, self.actor, actor_loss)
# target update
self.target_update(self.target_critic, self.critic)
if timestep % 100 and self.writer:
self.writer.add_scalar('Loss/Actor', actor_loss.item(), timestep)
self.writer.add_scalar('Loss/Critic', q1_loss.item(), timestep)
def save_weights(self, path):
self.actor.save(os.path.join(path, 'actor'))
self.critic.save(os.path.join(path, 'critic'))