Python model.ActorCritic() Examples
The following are 4
code examples of model.ActorCritic().
You can vote up the ones you like or vote down the ones you don't like,
and go to the original project or source file by following the links above each example.
You may also want to check out all available functions/classes of the module
model
, or try the search function
.
.
Example #1
| Source File: test.py From reinforcement-learning-pytorch with MIT License | 5 votes |
|
def main():
env = gym.make(args.env_name)
env.seed(500)
torch.manual_seed(500)
num_inputs = env.observation_space.shape[0]
num_actions = env.action_space.n
print('state size:', num_inputs)
print('action size:', num_actions)
net = ActorCritic(num_inputs, num_actions)
net.load_state_dict(torch.load(args.save_path + 'model.pth'))
net.to(device)
net.eval()
running_score = 0
steps = 0
for e in range(5):
done = False
score = 0
state = env.reset()
state = torch.Tensor(state).to(device)
state = state.unsqueeze(0)
while not done:
env.render()
steps += 1
policy, value = net(state)
action = get_action(policy, num_actions)
next_state, reward, done, _ = env.step(action)
next_state = torch.Tensor(next_state).to(device)
next_state = next_state.unsqueeze(0)
score += reward
state = next_state
print('{} episode | score: {:.2f}'.format(e, score))
Example #2
| Source File: test.py From reinforcement-learning-pytorch with MIT License | 5 votes |
|
def main():
env = gym.make(args.env_name)
env.seed(500)
torch.manual_seed(500)
num_inputs = env.observation_space.shape[0]
num_actions = env.action_space.n
print('state size:', num_inputs)
print('action size:', num_actions)
net = ActorCritic(num_inputs, num_actions)
net.load_state_dict(torch.load(args.save_path + 'model.pth'))
net.to(device)
net.eval()
running_score = 0
steps = 0
for e in range(5):
done = False
score = 0
state = env.reset()
state = torch.Tensor(state).to(device)
state = state.unsqueeze(0)
while not done:
env.render()
steps += 1
policy, value = net(state)
action = get_action(policy, num_actions)
next_state, reward, done, _ = env.step(action)
next_state = torch.Tensor(next_state).to(device)
next_state = next_state.unsqueeze(0)
score += reward
state = next_state
print('{} episode | score: {:.2f}'.format(e, score))
Example #3
| Source File: test.py From pytorch-a3c with MIT License | 4 votes |
|
def test(rank, args, shared_model, counter):
torch.manual_seed(args.seed + rank)
env = create_atari_env(args.env_name)
env.seed(args.seed + rank)
model = ActorCritic(env.observation_space.shape[0], env.action_space)
model.eval()
state = env.reset()
state = torch.from_numpy(state)
reward_sum = 0
done = True
start_time = time.time()
# a quick hack to prevent the agent from stucking
actions = deque(maxlen=100)
episode_length = 0
while True:
episode_length += 1
# Sync with the shared model
if done:
model.load_state_dict(shared_model.state_dict())
cx = torch.zeros(1, 256)
hx = torch.zeros(1, 256)
else:
cx = cx.detach()
hx = hx.detach()
with torch.no_grad():
value, logit, (hx, cx) = model((state.unsqueeze(0), (hx, cx)))
prob = F.softmax(logit, dim=-1)
action = prob.max(1, keepdim=True)[1].numpy()
state, reward, done, _ = env.step(action[0, 0])
done = done or episode_length >= args.max_episode_length
reward_sum += reward
# a quick hack to prevent the agent from stucking
actions.append(action[0, 0])
if actions.count(actions[0]) == actions.maxlen:
done = True
if done:
print("Time {}, num steps {}, FPS {:.0f}, episode reward {}, episode length {}".format(
time.strftime("%Hh %Mm %Ss",
time.gmtime(time.time() - start_time)),
counter.value, counter.value / (time.time() - start_time),
reward_sum, episode_length))
reward_sum = 0
episode_length = 0
actions.clear()
state = env.reset()
time.sleep(60)
state = torch.from_numpy(state)
Example #4
| Source File: train.py From reinforcement-learning-pytorch with MIT License | 4 votes |
|
def main():
env = gym.make(args.env_name)
env.seed(500)
torch.manual_seed(500)
num_inputs = env.observation_space.shape[0]
num_actions = env.action_space.n
print('state size:', num_inputs)
print('action size:', num_actions)
net = ActorCritic(num_inputs, num_actions)
optimizer = optim.Adam(net.parameters(), lr=0.001)
writer = SummaryWriter('logs')
if not os.path.isdir(args.save_path):
os.makedirs(args.save_path)
net.to(device)
net.train()
running_score = 0
for e in range(3000):
done = False
score = 0
state = env.reset()
state = torch.Tensor(state).to(device)
state = state.unsqueeze(0)
while not done:
if args.render:
env.render()
policy, value = net(state)
action = get_action(policy, num_actions)
next_state, reward, done, _ = env.step(action)
next_state = torch.Tensor(next_state).to(device)
next_state = next_state.unsqueeze(0)
mask = 0 if done else 1
reward = reward if not done or score == 499 else -1
transition = [state, next_state, action, reward, mask]
train_model(net, optimizer, transition, policy, value)
score += reward
state = next_state
score = score if score == 500.0 else score + 1
running_score = 0.99 * running_score + 0.01 * score
if e % args.log_interval == 0:
print('{} episode | score: {:.2f}'.format(e, running_score))
writer.add_scalar('log/score', float(score), running_score)
if running_score > args.goal_score:
ckpt_path = args.save_path + 'model.pth'
torch.save(net.state_dict(), ckpt_path)
print('running score exceeds 400 so end')
break
