def updatefig(*args):
nonlocal done
nonlocal obs
nonlocal HORIZON
nonlocal timestep
rebuild, obs_torch = transform_observation(obs)
if not done and timestep < HORIZON:
#action, action_proba = vape.act(obs_torch)
#action = action[0].detach().numpy()
action = env.action_space.sample()
action = [action[0], 0.3, 0.0]
obs, reward, done, info = env.step(action)
#env.render(mode='human')
timestep += 1
else:
done = False
obs = env.reset()
timestep = 0
c = NHWC(vape.centroids.detach().numpy())
im.set_array(
side_by_side(
side_by_side(side_by_side(obs, rebuild), c[0, :, :, 0]),
c[1, :, :, 0]))
vape.optimize_vae(obs_torch, optimizer)
time.sleep(0.01)
return im,
def show_centroids():
c = NHWC(vape.centroids.detach().numpy())
fig, ax = plt.subplots(nrows=2, ncols=2)
ax[0, 0].imshow(c[0, :, :, 0], cmap='Greys')
ax[0, 1].imshow(c[1, :, :, 0], cmap='Greys')
ax[1, 0].imshow(c[2, :, :, 0], cmap='Greys')
ax[1, 1].imshow(c[3, :, :, 0], cmap='Greys')
plt.show()
def updatefig(*args):
global done
global obs
if not done:
obs_torch = torch.from_numpy(NCHW([obs])).float().to(device)
mu, _ = vape.encode(obs_torch)
action, logp = vape.act(obs_torch)
obs, reward, done, info = env.step(action)
env.render(mode='human')
else:
done = False
obs = env.reset()
obs_torch = torch.from_numpy(NCHW([obs])).float().to(device)
rebuild, mu, log_sigma, z = vape.encode_decode(obs_torch)
rebuild = NHWC(rebuild.detach().numpy()[0])
im.set_array(side_by_side(obs, rebuild))
time.sleep(0.01)
return im,
def updatefig(*args):
nonlocal done
nonlocal obs
nonlocal HORIZON
nonlocal timestep
obs_torch = torch.from_numpy(NCHW([obs])).float().to(device)
if not done and timestep < HORIZON:
action, action_proba = vape.act(obs_torch)
action = action[0].detach().numpy()
obs, reward, done, info = env.step(action)
env.render(mode='human')
timestep += 1
else:
done = False
obs = env.reset()
timestep = 0
rebuild = vape.encode_decode(obs_torch)
rebuild = NHWC(rebuild.detach().numpy()[0])
im.set_array(side_by_side(obs, rebuild))
vape.optimize_vae(obs_torch, optimizer)
time.sleep(0.01)
return im,
def updatefig(*args):
global done
global obs
if not done:
if policy:
obs_torch = torch.from_numpy(NCHW([obs])).float().to(device)
mu, _ = vae.encode(obs_torch)
action, action_proba = policy.act(mu.detach().numpy())
action = action[0]
else:
action = env.action_space.sample()
action = [action[0], 0.3, 0.0]
obs, reward, done, info = env.step(action)
env.render(mode='human')
else:
done = False
obs = env.reset()
obs_torch = torch.from_numpy(NCHW([obs])).float().to(device)
mu, log_sigma, z, rebuild = vae(obs_torch)
rebuild = NHWC(rebuild.detach().numpy()[0])
im.set_array(side_by_side(obs, rebuild))
time.sleep(0.01)
return im,
def main():
# Parse arguments
parser = argparse.ArgumentParser(description='REINFORCE using PyTorch')
# Logging
parser.add_argument('--alias',
type=str,
default='base',
help="""Alias of the model.""")
parser.add_argument('--render_interval',
type=int,
default=100,
help='interval between rendered epochs (default: 100)')
# Learning parameters
parser.add_argument('--gamma',
type=float,
default=0.99,
help='discount factor (default: 0.99)')
parser.add_argument('--lr',
type=float,
default=0.01,
help='learning rate (default: 0.01)')
parser.add_argument('--no-cuda',
action='store_true',
default=False,
help='Enables CUDA training')
parser.add_argument('--eb',
type=int,
default=1,
help='episode batch (default: 1)')
parser.add_argument('--episodes',
type=int,
default=10000,
help='simulated episodes (default: 10000)')
parser.add_argument('--policy',
type=str,
default=None,
help="""Policy checkpoint to restore.""")
parser.add_argument('--seed',
type=int,
default=42,
help='random seed (default: 42)')
parser.add_argument('--horizon',
type=int,
default=1000,
help='horizon (default: 1000)')
parser.add_argument('--baseline',
action='store_true',
help='use the baseline for the REINFORCE algorithm')
args = parser.parse_args()
# Check cuda
args.cuda = not args.no_cuda and torch.cuda.is_available()
device = torch.device("cuda" if args.cuda else "cpu")
# Initialize environment
env = gym.make('CarRacing-v0')
env = CropCarRacing(env)
env = ResizeObservation(env, (32, 32, 3))
env = Scolorized(env, weights=[0.0, 1.0, 0.0])
env = NormalizeRGB(env)
env.seed(args.seed)
torch.manual_seed(args.seed)
print("Env final goal:", env.spec.reward_threshold)
# Create the alias for the run
alias = '%s_%s' % (args.alias, time.time())
# Use alias for checkpoints
checkpoint_best_filename = 'policy_weights/' + alias + '_best.torch'
checkpoint_final_filename = 'policy_weights/' + alias + '_final.torch'
if not os.path.exists('weights/'):
os.makedirs('weights/')
# Tensorboard writer
writer = SummaryWriter('logs/' + alias)
# Create VAE policy
vape = VAEPolicy()
optimizer = optim.Adam(vape.parameters(), lr=1e-04)
# Animation of environment
obs = env.reset()
obs_torch = torch.from_numpy(NCHW([obs])).float().to(device)
rebuild = vape.encode_decode(obs_torch)
rebuild = NHWC(rebuild.detach().numpy()[0])
fig1 = plt.figure()
if len(obs.shape) == 3 and (obs.shape[-1] == 1):
im = plt.imshow(side_by_side(obs, rebuild), cmap="Greys")
else:
im = plt.imshow(side_by_side(obs, rebuild))
done = False
HORIZON = 200
timestep = 0
# Setting animation update function
def updatefig(*args):
nonlocal done
nonlocal obs
nonlocal HORIZON
nonlocal timestep
obs_torch = torch.from_numpy(NCHW([obs])).float().to(device)
if not done and timestep < HORIZON:
action, action_proba = vape.act(obs_torch)
action = action[0].detach().numpy()
obs, reward, done, info = env.step(action)
env.render(mode='human')
timestep += 1
else:
done = False
obs = env.reset()
timestep = 0
rebuild = vape.encode_decode(obs_torch)
rebuild = NHWC(rebuild.detach().numpy()[0])
im.set_array(side_by_side(obs, rebuild))
vape.optimize_vae(obs_torch, optimizer)
time.sleep(0.01)
return im,
# Start animation
ani = animation.FuncAnimation(fig1, updatefig, interval=50, blit=True)
plt.show()
# Close env and writer
env.close()
writer.close()
env = Scolorized(env, weights=[0.0, 1.0, 0.0])
env.seed(args.seed)
# Network creation
VAE_class = VAEbyArch(args.arch)
vae = VAE_class(latent_size=args.latent_size).to(device)
# Restore checkpoint
assert args.vae, "No checkpoint provided."
vae.load_state_dict(torch.load(args.vae))
vae.eval()
if args.dataset:
# Single observation display
mu, log_sigma, z, rebuild = vae(dataset_torch[args.sample:args.sample+1])
rebuild = rebuild.detach().numpy()[0]
imshow_bw_or_rgb(side_by_side(dataset[args.sample], NHWC(rebuild)))
plt.show()
else:
# Check if we use a policy
policy = None
if args.policy and args.vae_old:
policy_env = VAEObservation(env, args.vae_old, arch=args.arch)
policy = Policy(policy_env)
policy.load_state_dict(torch.load(args.policy))
policy.eval()
vae_old = VAE_class(latent_size=args.latent_size).to(device)
vae_old.load_state_dict(torch.load(args.vae_old))
vae_old.eval()
# Animation of environment
obs = env.reset()
obs_torch = torch.from_numpy(NCHW([obs])).float().to(device)
def transform_observation(obs):
obs_torch = torch.from_numpy(NCHW([obs])).float().to(device)
rebuild = vape.encode_decode(obs_torch)
rebuild = NHWC(rebuild.detach().numpy()[0])
return rebuild, obs_torch
env = ResizeObservation(env, (64, 64, 3))
env = NormalizeRGB(env)
env = Scolorized(env, weights=[0.0, 1.0, 0.0])
env.seed(args.seed)
vape = VAEPolicy()
# Restore checkpoint
assert args.controller, "No checkpoint provided."
vape.load_state_dict(torch.load(args.controller))
vape.eval()
# Animation of environment
obs = env.reset()
obs_torch = torch.from_numpy(NCHW([obs])).float().to(device)
rebuild, mu, log_sigma, z = vape.encode_decode(obs_torch)
rebuild = NHWC(rebuild.detach().numpy()[0])
fig1 = plt.figure()
if len(obs.shape) == 3 and (obs.shape[-1] == 1):
im = plt.imshow(side_by_side(obs, rebuild), cmap="Greys")
else:
im = plt.imshow(side_by_side(obs, rebuild))
done = False
# Setting animation update function
def updatefig(*args):
global done
global obs
if not done:
obs_torch = torch.from_numpy(NCHW([obs])).float().to(device)
mu, _ = vape.encode(obs_torch)