def train(rank, args, shared_model, optimizer, counter, lock):
env = gym.make(args.env_name)
env.seed(args.seed + rank)
torch.manual_seed(args.seed + rank)
model = Policy(2, action_map)
model.train()
state = env.reset()
# state = tensor_state(state)
done = True
episode_length = 0
while True:
# Sync with the shared model
model.load_state_dict(shared_model.state_dict())
if done:
cx = torch.zeros(1, 64)
hx = torch.zeros(1, 64)
else:
cx = cx.data
hx = hx.data
values = []
log_probs = []
rewards = []
entropies = []
for step in range(args.num_steps):
episode_length += 1
action, hx, cx = model(state, hx, cx)
entropies.append(model.entropy)
state, reward, done, _ = env.step(action)
reward = max(min(reward, 1), -1)
with lock:
counter.value += 1
if done:
episode_length = 0
state = env.reset()
values.append(model.v)
log_probs.append(model.log_prob)
rewards.append(reward)
if done:
break
R = torch.zeros(1, 1)
if not done:
model(state, hx, cx)
R = model.v.data
values.append(R)
policy_loss = 0
value_loss = 0
gae = torch.zeros(1, 1)
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] * gae - args.entropy_coef * entropies[i]
loss = policy_loss + args.value_loss_coef * value_loss
optimizer.zero_grad()
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), args.max_grad_norm)
ensure_shared_grads(model, shared_model)
optimizer.step()
def train():
env = gym.make('Pong-v0')
# env.seed(args.seed + rank)
# torch.manual_seed(args.seed + rank)
model = Policy(2, action_map)
model.train()
# model.eval()
optimizer = optim.Adam(model.parameters())
state = env.reset()
# state = tensor_state(state)
done = True
episode_length = 0
while True:
if done:
model.reset_hidden()
values = []
log_probs = []
rewards = []
entropies = []
while True:
episode_length += 1
action = model(state)
entropies.append(model.entropy)
state, reward, done, _ = env.step(action)
reward = max(min(reward, 1), -1)
if done:
episode_length = 0
state = env.reset()
values.append(model.v)
log_probs.append(model.log_prob)
rewards.append(reward)
if done:
break
R = torch.zeros(1, 1)
values.append(R)
policy_loss = 0
value_loss = 0
gae = torch.zeros(1, 1)
for i in reversed(range(len(rewards))):
R = 0.99 * R + rewards[i]
advantage = R - values[i]
value_loss = value_loss + 0.5 * advantage.pow(2)
# Generalized Advantage Estimataion
delta_t = rewards[i] + 0.99 * values[i + 1].data - values[i].data
gae = gae * 0.99 + delta_t
policy_loss = policy_loss - log_probs[i] * gae - 0.01 * entropies[i]
loss = policy_loss + 0.5 * value_loss
optimizer.zero_grad()
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), 50)
# ensure_shared_grads(model, shared_model)
optimizer.step()
print('loss:', loss)