shared_model = ActorCritic(env.observation_space.shape[0], len(ACTIONS))
shared_model.share_memory()
shared_scme = SCME(env.observation_space.shape[0], len(ACTIONS))
shared_scme.share_memory()
if os.path.isfile(SAVEPATH):
print('Loading A3C parametets & SCME parameters...')
shared_model.load_state_dict(torch.load(SAVEPATH))
shared_scme.load_state_dict(torch.load(SAVEPATH[:-4] + '_scme.pkl'))
torch.manual_seed(args.seed)
#optimizer = torch.optim.Adam(list(shared_model.parameters()) + list(shared_scme.parameters()), lr=args.lr)
optimizer = SharedAdam(list(shared_model.parameters()) +
list(shared_scme.parameters()),
lr=args.lr)
optimizer.share_memory()
# optimizer_icm = SharedAdam(shared_icm.parameters(), lr=args.lr)
# optimizer_icm.share_memory()
print("No of available cores : {}".format(mp.cpu_count()))
processes = []
counter = mp.Value('i', 0)
lock = mp.Lock()
p = mp.Process(target=test,
def train(rank,
args,
shared_model,
counter,
lock,
optimizer=None,
select_sample=True):
torch.manual_seed(args.seed + rank)
print("Process No : {} | Sampling : {}".format(rank, select_sample))
FloatTensor = torch.cuda.FloatTensor if args.use_cuda else torch.FloatTensor
DoubleTensor = torch.cuda.DoubleTensor if args.use_cuda else torch.DoubleTensor
ByteTensor = torch.cuda.ByteTensor if args.use_cuda else torch.ByteTensor
env = create_mario_env(args.env_name)
env.seed(args.seed + rank)
model = ActorCritic(env.observation_space.shape[0], len(ACTIONS))
if args.use_cuda:
model.cuda()
if optimizer is None:
optimizer = optim.Adam(shared_model.parameters(), lr=args.lr)
model.train()
state = env.reset()
state = torch.from_numpy(state)
done = True
episode_length = 0
for num_iter in count():
if rank == 0:
env.render()
if num_iter % args.save_interval == 0 and num_iter > 0:
print("Saving model at :" + args.save_path)
torch.save(shared_model.state_dict(), args.save_path)
if num_iter % (
args.save_interval * 2.5
) == 0 and num_iter > 0 and rank == 1: # Second saver in-case first processes crashes
print("Saving model for process 1 at :" + args.save_path)
torch.save(shared_model.state_dict(), args.save_path)
# Sync with the shared model
model.load_state_dict(shared_model.state_dict())
if done:
cx = Variable(torch.zeros(1, 512)).type(FloatTensor)
hx = Variable(torch.zeros(1, 512)).type(FloatTensor)
else:
cx = Variable(cx.data).type(FloatTensor)
hx = Variable(hx.data).type(FloatTensor)
values = []
log_probs = []
rewards = []
entropies = []
reason = ''
for step in range(args.num_steps):
episode_length += 1
state_inp = Variable(state.unsqueeze(0)).type(FloatTensor)
value, logit, (hx, cx) = model((state_inp, (hx, cx)))
prob = F.softmax(logit, dim=-1)
log_prob = F.log_softmax(logit, dim=-1)
entropy = -(log_prob * prob).sum(-1, keepdim=True)
entropies.append(entropy)
if select_sample:
action = prob.multinomial().data
else:
action = prob.max(-1, keepdim=True)[1].data
log_prob = log_prob.gather(-1, Variable(action))
action_out = ACTIONS[action][0, 0]
# print("Process: {} Action: {}".format(rank, str(action_out)))
state, reward, done, _ = env.step(action_out)
done = done or episode_length >= args.max_episode_length
reward = max(min(reward, 50), -50)
with lock:
counter.value += 1
if done:
episode_length = 0
env.change_level(0)
state = env.reset()
print("Process {} has completed.".format(rank))
env.locked_levels = [False] + [True] * 31
state = torch.from_numpy(state)
values.append(value)
log_probs.append(log_prob)
rewards.append(reward)
if done:
break
R = torch.zeros(1, 1)
if not done:
state_inp = Variable(state.unsqueeze(0)).type(FloatTensor)
value, _, _ = model((state_inp, (hx, cx)))
R = value.data
values.append(Variable(R).type(FloatTensor))
policy_loss = 0
value_loss = 0
R = Variable(R).type(FloatTensor)
gae = torch.zeros(1, 1).type(FloatTensor)
for i in reversed(range(len(rewards))):
R = args.gamma * R + rewards[i]
advantage = R - values[i]
value_loss = value_loss + 0.5 * advantage.pow(2)
# Generalized Advantage Estimataion
delta_t = rewards[i] + args.gamma * \
values[i + 1].data - values[i].data
gae = gae * args.gamma * args.tau + delta_t
policy_loss = policy_loss - \
log_probs[i] * Variable(gae).type(FloatTensor) - args.entropy_coef * entropies[i]
total_loss = policy_loss + args.value_loss_coef * value_loss
print("Process {} loss :".format(rank), total_loss.data)
# print("Process: {} Episode: {}".format(rank, str(episode_length)))
optimizer.zero_grad()
(total_loss).backward()
torch.nn.utils.clip_grad_norm(model.parameters(), args.max_grad_norm)
ensure_shared_grads(model, shared_model)
optimizer.step()
print("Process {} closed.".format(rank))
args = parser.parse_args()
env = create_mario_env(args.env_name)
shared_model = ActorCritic( env.observation_space.shape[0], len(COMPLEX_MOVEMENT))
if args.use_cuda:
shared_model.cuda()
shared_model.share_memory()
if os.path.isfile(args.save_path):
print('Loading A3C parametets ...')
shared_model.load_state_dict(torch.load(args.save_path, map_location='cpu'))
torch.manual_seed(args.seed)
optimizer = SharedAdam(shared_model.parameters(), lr=args.lr)
optimizer.share_memory()
print (color.BLUE + "No of available cores : {}".format(mp.cpu_count()) + color.END)
processes = []
counter = mp.Value('i', 0)
lock = mp.Lock()
#test(0, args, shared_model, counter)
# To test a pretrained model
p = mp.Process(target=test, args=(args.num_processes, args, shared_model, counter))
p.start()
processes.append(p)
def train(rank, args, shared_model, shared_scme, counter, lock, optimizer=None, select_sample=True):
torch.manual_seed(args.seed + rank)
print("Process No : {} | Sampling : {}".format(rank, select_sample))
FloatTensor = torch.FloatTensor# torch.cuda.FloatTensor if args.use_cuda else torch.FloatTensor
DoubleTensor = torch.DoubleTensor# torch.cuda.DoubleTensor if args.use_cuda else torch.DoubleTensor
ByteTensor = torch.ByteTensor# torch.cuda.ByteTensor if args.use_cuda else torch.ByteTensor
savefile = os.getcwd() + '/save/scmemi_'+ args.reward_type +'/train_reward.csv'
saveweights = os.getcwd() + '/save/scmemi_'+ args.reward_type +'/mario_a3c_params.pkl'
env = create_mario_env(args.env_name, args.reward_type)
#env.seed(args.seed + rank)
model = ActorCritic(env.observation_space.shape[0], len(ACTIONS))
if optimizer is None:
optimizer = optim.Adam(list(shared_model.parameters()) + list(shared_scme.parameters()), lr=args.lr)
scme_model = SCME(env.observation_space.shape[0], len(ACTIONS))
model.train()
scme_model.train()
state = env.reset()
cum_rew = 0
state = torch.from_numpy(state)
done = True
episode_length = 0
for num_iter in count():
#env.render()
if rank == 0:
if num_iter % args.save_interval == 0 and num_iter > 0:
print ("Saving model at :" + saveweights)
torch.save(shared_model.state_dict(), saveweights)
torch.save(shared_scme.state_dict(), saveweights[:-4] + '_scme.pkl')
if num_iter % (args.save_interval * 2.5) == 0 and num_iter > 0 and rank == 1: # Second saver in-case first processes crashes
print ("Saving model for process 1 at :" + saveweights)
torch.save(shared_model.state_dict(), saveweights)
torch.save(shared_scme.state_dict(), saveweights[:-4] + '_scme.pkl')
# Sync with the shared model
model.load_state_dict(shared_model.state_dict())
scme_model.load_state_dict(shared_scme.state_dict())
if done:
cx = Variable(torch.zeros(1, 512)).type(FloatTensor)
hx = Variable(torch.zeros(1, 512)).type(FloatTensor)
else:
cx = Variable(cx.data).type(FloatTensor)
hx = Variable(hx.data).type(FloatTensor)
values = []
log_probs = []
rewards = []
entropies = []
vae_losses = []
cur_losses = []
mi_losses = []
#reason =''
for step in range(args.num_steps):
episode_length += 1
state_inp = Variable(state.unsqueeze(0)).type(FloatTensor)
value, logit, (hx, cx) = model((state_inp, (hx, cx)))
prob = F.softmax(logit, dim=-1)
log_prob = F.log_softmax(logit, dim=-1)
entropy = -(log_prob * prob).sum(-1, keepdim=True)
entropies.append(entropy)
if select_sample:
action = prob.multinomial(1).data
else:
action = prob.max(-1, keepdim=True)[1].data
log_prob = log_prob.gather(-1, Variable(action))
action_out = int(action[0, 0].data.numpy())
state, reward, done, info = env.step(action_out)
cum_rew = cum_rew + reward
action_one_hot = (torch.eye(len(ACTIONS))[action_out]).view(1,-1)
next_state_inp = Variable(torch.from_numpy(state).unsqueeze(0)).type(FloatTensor)
pred_z, mi, mi1, actual_z, xt1_hat, xt1, xt1_mu, xt1_logvar = scme_model((state_inp, next_state_inp, action_one_hot))
vae_loss = loss_function(xt1_hat, xt1, xt1_mu, xt1_logvar)
cur_loss = ((pred_z - actual_z).pow(2)).sum(-1, keepdim=True)/2/50
mi_loss = mutual(mi, mi1).sum(-1, keepdim=True)/10
done = done or episode_length >= args.max_episode_length
cur_reward = (args.alpha*cur_loss).data.numpy()[0,0]
mi_reward = (args.beta*mi_loss).data.numpy()
reward = cur_reward + reward + mi_reward
reward = max(min(reward, 50), -5)
with lock:
counter.value += 1
if done:
episode_length = 0
# env.change_level(0)
state = env.reset()
with open(savefile[:-4]+'_{}.csv'.format(rank), 'a', newline='') as sfile:
writer = csv.writer(sfile)
writer.writerows([[cum_rew, info['x_pos']/x_norm]])
cum_rew = 0
# print ("Process {} has completed.".format(rank))
# env.locked_levels = [False] + [True] * 31
state = torch.from_numpy(state)
values.append(value)
log_probs.append(log_prob)
rewards.append(reward)
vae_losses.append(vae_loss)
cur_losses.append(cur_loss)
mi_losses.append(mi_loss)
if done:
break
R = torch.zeros(1, 1)
if not done:
state_inp = Variable(state.unsqueeze(0)).type(FloatTensor)
value, _, _ = model((state_inp, (hx, cx)))
R = value.data
values.append(Variable(R).type(FloatTensor))
policy_loss = 0
value_loss = 0
scme_loss = 0
R = Variable(R).type(FloatTensor)
gae = torch.zeros(1, 1).type(FloatTensor)
for i in reversed(range(len(rewards))):
R = args.gamma * R + rewards[i]
advantage = R - values[i]
value_loss = value_loss + 0.5 * advantage.pow(2)
# Generalized Advantage Estimataion
delta_t = rewards[i] + args.gamma * values[i + 1].data - values[i].data
gae = gae * args.gamma * args.tau + delta_t
policy_loss = policy_loss - log_probs[i] * Variable(gae).type(FloatTensor) - args.entropy_coef * entropies[i]
scme_loss = 0.01*vae_losses[i] + cur_losses[i] - mi_losses[i]
total_loss = args.lambd*(policy_loss + args.value_loss_coef * value_loss)
# print ("Process {} loss :".format(rank), total_loss.data)
optimizer.zero_grad()
# cur_optimizer.zero_grad()
(total_loss + scme_loss).backward()
# (curiosity_loss).backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), args.max_grad_norm)
torch.nn.utils.clip_grad_norm_(scme_model.parameters(), args.max_grad_norm)
ensure_shared_grads(model, shared_model)
ensure_shared_grads(scme_model, shared_scme)
optimizer.step()
class PPO:
def __init__(
self,
lr,
gamma,
k_epochs,
eps_clip,
n_j,
n_m,
num_layers,
neighbor_pooling_type,
input_dim,
hidden_dim,
num_mlp_layers_feature_extract,
num_mlp_layers_actor,
hidden_dim_actor,
num_mlp_layers_critic,
hidden_dim_critic,
):
self.lr = lr
self.gamma = gamma
self.eps_clip = eps_clip
self.k_epochs = k_epochs
self.policy = ActorCritic(
n_j=n_j,
n_m=n_m,
num_layers=num_layers,
learn_eps=False,
neighbor_pooling_type=neighbor_pooling_type,
input_dim=input_dim,
hidden_dim=hidden_dim,
num_mlp_layers_feature_extract=num_mlp_layers_feature_extract,
num_mlp_layers_actor=num_mlp_layers_actor,
hidden_dim_actor=hidden_dim_actor,
num_mlp_layers_critic=num_mlp_layers_critic,
hidden_dim_critic=hidden_dim_critic,
device=device)
self.policy_old = deepcopy(self.policy)
'''self.policy.load_state_dict(
torch.load(path='./{}.pth'.format(str(n_j) + '_' + str(n_m) + '_' + str(1) + '_' + str(99))))'''
self.policy_old.load_state_dict(self.policy.state_dict())
self.optimizer = torch.optim.Adam(self.policy.parameters(), lr=lr)
self.scheduler = torch.optim.lr_scheduler.StepLR(
self.optimizer,
step_size=configs.decay_step_size,
gamma=configs.decay_ratio)
self.V_loss_2 = nn.MSELoss()
def update(self, memories, n_tasks, g_pool):
vloss_coef = configs.vloss_coef
ploss_coef = configs.ploss_coef
entloss_coef = configs.entloss_coef
rewards_all_env = []
adj_mb_t_all_env = []
fea_mb_t_all_env = []
candidate_mb_t_all_env = []
mask_mb_t_all_env = []
a_mb_t_all_env = []
old_logprobs_mb_t_all_env = []
# store data for all env
for i in range(len(memories)):
rewards = []
discounted_reward = 0
for reward, is_terminal in zip(reversed(memories[i].r_mb),
reversed(memories[i].done_mb)):
if is_terminal:
discounted_reward = 0
discounted_reward = reward + (self.gamma * discounted_reward)
rewards.insert(0, discounted_reward)
rewards = torch.tensor(rewards, dtype=torch.float).to(device)
rewards = (rewards - rewards.mean()) / (rewards.std() + 1e-5)
rewards_all_env.append(rewards)
# process each env data
adj_mb_t_all_env.append(
aggr_obs(torch.stack(memories[i].adj_mb).to(device), n_tasks))
fea_mb_t = torch.stack(memories[i].fea_mb).to(device)
fea_mb_t = fea_mb_t.reshape(-1, fea_mb_t.size(-1))
fea_mb_t_all_env.append(fea_mb_t)
candidate_mb_t_all_env.append(
torch.stack(memories[i].candidate_mb).to(device).squeeze())
mask_mb_t_all_env.append(
torch.stack(memories[i].mask_mb).to(device).squeeze())
a_mb_t_all_env.append(
torch.stack(memories[i].a_mb).to(device).squeeze())
old_logprobs_mb_t_all_env.append(
torch.stack(
memories[i].logprobs).to(device).squeeze().detach())
# get batch argument for net forwarding: mb_g_pool is same for all env
mb_g_pool = g_pool_cal(
g_pool,
torch.stack(memories[0].adj_mb).to(device).shape, n_tasks, device)
# Optimize policy for K epochs:
for _ in range(self.k_epochs):
loss_sum = 0
vloss_sum = 0
for i in range(len(memories)):
pis, vals = self.policy(x=fea_mb_t_all_env[i],
graph_pool=mb_g_pool,
adj=adj_mb_t_all_env[i],
candidate=candidate_mb_t_all_env[i],
mask=mask_mb_t_all_env[i],
padded_nei=None)
logprobs, ent_loss = eval_actions(pis.squeeze(),
a_mb_t_all_env[i])
ratios = torch.exp(logprobs -
old_logprobs_mb_t_all_env[i].detach())
advantages = rewards_all_env[i] - vals.detach()
surr1 = ratios * advantages
surr2 = torch.clamp(ratios, 1 - self.eps_clip,
1 + self.eps_clip) * advantages
v_loss = self.V_loss_2(vals.squeeze(), rewards_all_env[i])
p_loss = -torch.min(surr1, surr2)
ent_loss = -ent_loss.clone()
loss = vloss_coef * v_loss + ploss_coef * p_loss + entloss_coef * ent_loss
loss_sum += loss
vloss_sum += v_loss
self.optimizer.zero_grad()
loss_sum.mean().backward()
self.optimizer.step()
# Copy new weights into old policy:
self.policy_old.load_state_dict(self.policy.state_dict())
if configs.decayflag:
self.scheduler.step()
return loss_sum.mean().item(), vloss_sum.mean().item()
