def make_env(seed):
env = gym.make('CarRacing-v0')
env = NormalizeRGB(env)
env = CropCarRacing(env)
env = ResizeObservation(env, (64, 64, 3))
env.seed(seed)
np.random.seed(seed)
return env
def run_agent(layout: str):
env = PacmanEnv(layout)
env = SkipFrame(env, skip=4)
env = GrayScaleObservation(env)
env = ResizeObservation(env, shape=84)
env = FrameStack(env, num_stack=4)
screen = env.reset(mode='rgb_array')
n_actions = env.action_space.n
model = load_model(screen.shape, n_actions, 'pacman.pth')
for i in range(10):
env.render(mode='human')
screen = env.reset(mode='rgb_array')
for _ in count():
env.render(mode='human')
action = select_action(screen, 0, model, n_actions)
screen, reward, done, info = env.step(action)
if done:
break
def main():
# Parse arguments
parser = argparse.ArgumentParser(description='REINFORCE using PyTorch')
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('--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('--render',
action='store_true',
help='render the environment')
parser.add_argument('--baseline',
action='store_true',
help='use the baseline for the REINFORCE algorithm')
parser.add_argument('--render_interval',
type=int,
default=100,
help='interval between rendered epochs (default: 100)')
parser.add_argument('--env',
type=str,
default='CarRacing-v0',
help='environment to train on (default: CartPole-v0)')
parser.add_argument('--vae',
type=str,
default=None,
help='VAE checkpoint to load')
parser.add_argument('--arch',
type=str,
default='base_car_racing',
help="""Model architecture.""")
args = parser.parse_args()
# Initialize environment
env = gym.make(args.env)
env = CropCarRacing(env)
env = ResizeObservation(env, (32, 32, 3))
env = Scolorized(env, weights=[0.0, 1.0, 0.0])
env = NormalizeRGB(env)
env = VAEObservation(env, args.vae, arch=args.arch)
print(env.observation_space)
env.seed(args.seed)
torch.manual_seed(args.seed)
print("Env final goal:", env.spec.reward_threshold)
# Create the alias for the run
alias = 'reinforce_lr=%s_eb=%s_seed=%s' % (args.lr, args.eb, args.seed)
if args.baseline:
alias += '_baseline'
alias += '_%s' % (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('policy_weights/'):
os.makedirs('policy_weights/')
# Tensorboard writer
writer = SummaryWriter('policy_logs/' + alias)
# Declare policy
policy = Policy(env)
if args.policy:
policy.load_state_dict(torch.load(args.policy))
policy.eval()
# Declare sampler
sampler = Sampler(env, args.horizon)
# Run episodes
running_reward = deque(maxlen=100)
best_reward = None
for i_episode in trange(0,
args.episodes,
args.eb,
desc="Episodes",
unit_scale=args.eb):
# Sample trajectories
trajectories = sampler.sample(args.eb,
policy,
render=(i_episode %
args.render_interval == 0))
# Update policy
finish_episode(trajectories, policy, args)
# Get quantities for summaries
episode_rewards = np.sum(trajectories['rewards'], axis=1)
mean_reward = np.mean(episode_rewards)
episode_lens = np.sum(trajectories['mask'], axis=1)
for sub_i in range(args.eb):
# Summaries: mean episode reward for 100 episodes
running_reward.append(episode_rewards[sub_i])
writer.add_scalar('data/mean_100episode_reward',
np.mean(running_reward), i_episode + sub_i)
# Summaries: mean episode len
writer.add_scalar('data/episode_len', episode_lens[sub_i],
i_episode + sub_i)
writer.add_scalar('data/episode_reward', episode_rewards[sub_i],
i_episode + sub_i)
# Save best model if needed
if (best_reward is None) or (mean_reward > best_reward):
best_reward = mean_reward
print("Saving best model:", best_reward)
torch.save(policy.state_dict(), checkpoint_best_filename)
# Check if completed
if np.mean(running_reward) > env.spec.reward_threshold:
print("Solved, stopping. Mean reward:", np.mean(running_reward))
break
# Save final model
torch.save(policy.state_dict(), checkpoint_final_filename)
# Close env and writer
env.close()
writer.close()
from metrics import MetricLogger
from agent import Mario
from wrappers import ResizeObservation, SkipFrame
# Initialize Super Mario environment
env = gym_super_mario_bros.make('SuperMarioBros-1-1-v0')
# Limit the action-space to
# 0. walk right
# 1. jump right
env = JoypadSpace(env, [['right'], ['right', 'A']])
# Apply Wrappers to environment
env = SkipFrame(env, skip=4)
env = GrayScaleObservation(env, keep_dim=False)
env = ResizeObservation(env, shape=84)
env = TransformObservation(env, f=lambda x: x / 255.)
env = FrameStack(env, num_stack=4)
env.reset()
save_dir = Path('checkpoints') / datetime.datetime.now().strftime(
'%Y-%m-%dT%H-%M-%S')
save_dir.mkdir(parents=True)
checkpoint = None # Path('checkpoints/2020-10-21T18-25-27/mario.chkpt')
# Add in check to see if GPU is avaliable (BM)
if torch.cuda.is_available():
device = torch.device("cuda:0")
print("Using GPU!")
def train_agent(layout: str, episodes: int = 10000, frames_to_skip: int = 4):
GAMMA = 0.99
EPSILON = 1.0
EPS_END = 0.1
EPS_DECAY = 1e7
TARGET_UPDATE = 10
BATCH_SIZE = 64
epsilon_by_frame = lambda frame_idx: EPS_END + (
EPSILON - EPS_END) * math.exp(-1. * frame_idx / EPS_DECAY)
# Get screen size so that we can initialize layers correctly based on shape
# returned from AI gym. Typical dimensions at this point are close to 3x40x90
# which is the result of a clamped and down-scaled render buffer in get_screen()
env = PacmanEnv(layout=layout)
env = SkipFrame(env, skip=frames_to_skip)
env = GrayScaleObservation(env)
env = ResizeObservation(env, shape=84)
env = FrameStack(env, num_stack=4)
screen = env.reset(mode='rgb_array')
# Get number of actions from gym action space
n_actions = env.action_space.n
policy_net = DQN(screen.shape, n_actions).to(device)
target_net = DQN(screen.shape, n_actions).to(device)
target_net.load_state_dict(policy_net.state_dict())
target_net.eval()
optimizer = optim.RMSprop(policy_net.parameters())
memory = ReplayBuffer(BATCH_SIZE)
for i_episode in range(episodes):
# Initialize the environment and state
state = env.reset(mode='rgb_array')
ep_reward = 0.
EPSILON = epsilon_by_frame(i_episode)
for t in count():
# Select and perform an action
env.render(mode='human')
action = select_action(state, EPSILON, policy_net, n_actions)
next_state, reward, done, info = env.step(action)
reward = max(-1.0, min(reward, 1.0))
ep_reward += reward
memory.cache(state, next_state, action, reward, done)
# Observe new state
if done:
next_state = None
# Move to the next state
state = next_state
# Perform one step of the optimization (on the target network)
optimize_model(memory, policy_net, optimizer, target_net, GAMMA)
if done:
print("Episode #{}, lasts for {} timestep, total reward: {}".
format(i_episode, t + 1, ep_reward))
break
# Update the target network, copying all weights and biases in DQN
if i_episode % TARGET_UPDATE == 0:
target_net.load_state_dict(policy_net.state_dict())
if i_episode % 1000 == 0:
save_model(target_net, 'pacman.pth')
print('Complete')
env.render()
env.close()
save_model(target_net, 'pacman.pth')
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()
# Check cuda
args.cuda = not args.no_cuda and torch.cuda.is_available()
device = torch.device("cuda" if args.cuda else "cpu")
# Loading the dataset
if args.dataset:
dataset = np.array(pickle.load(open(args.dataset, 'rb')))
N_SAMPLES, W, H, CHANNELS = dataset.shape
print("Dataset size:", N_SAMPLES)
print("Channels:", CHANNELS)
print("Image dim: (%d,%d)" % (W,H))
dataset_torch = torch.from_numpy(NCHW(dataset)).float().to(device)
else:
print("Using gym environment directly.")
env = gym.make('CarRacing-v0')
env = CropCarRacing(env)
env = ResizeObservation(env, (32, 32, 3))
env = NormalizeRGB(env)
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])
import matplotlib.pyplot as plt
from tqdm import trange
from wrappers import ResizeObservation, CropCarRacing, Scolorized, NormalizeRGB
'''
Car Racing action space:
Box(3) floats
action[0]: steer, -1 to 1
action[1]: gas. 0 to 1
action[2]: brake, 0 to 1
'''
env = gym.make('CarRacing-v0')
env = CropCarRacing(env)
env = ResizeObservation(env, (64, 64, 3))
#env = Scolorized(env)
env = NormalizeRGB(env)
dataset = []
env.seed(42)
obs = env.reset()
done = False
print(env.observation_space)
print(env.action_space)
for i in trange(50):
action = env.action_space.sample()
obs, reward, done, info = env.step(action)
env.render()
def main():
# Parse arguments
parser = argparse.ArgumentParser(description='REINFORCE using PyTorch')
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('--vae_lr', type=float, default=1e-04, help='learning rate (default: 0.01)')
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('--controller', type=str, default=None, help="""Controller 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('--render', action='store_true', help='render the environment')
parser.add_argument('--baseline', action='store_true', help='use the baseline for the REINFORCE algorithm')
parser.add_argument('--render_interval', type=int, default=100, help='interval between rendered epochs (default: 100)')
parser.add_argument('--avoidance', type=str, default='self', help='Avoidance scheme')
parser.add_argument('--dist', type=str, default='beta', help='Action probability distribution.')
parser.add_argument('--avoidance_max', type=float, default=1.0, help='Avoidance max value')
args = parser.parse_args()
# Initialize environment
env = gym.make('CarRacing-v0')
env = CropCarRacing(env)
env = ResizeObservation(env, (64, 64, 3))
env = Scolorized(env, weights=[0.0, 1.0, 0.0])
env = NormalizeRGB(env)
#env = ActionScaler(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 = 'reinforce_lr=%s_eb=%s_seed=%s' % (args.lr, args.eb, args.seed)
if args.baseline:
alias += '_baseline'
alias += '_%s' % (time.time())
# Use alias for checkpoints
checkpoint_best_filename = 'weights/' + alias + '_best.torch'
checkpoint_final_filename = 'weights/' + alias + '_final.torch'
if not os.path.exists('weights/'):
os.makedirs('weights/')
# Tensorboard writer
writer = SummaryWriter('logs/' + alias)
# Declare vae policy
vape = VAEPolicy(avoidance=args.avoidance, avoidance_threshold=args.avoidance_max, vae_lr=args.vae_lr)
if args.controller:
vape.load_state_dict(torch.load(args.controller))
# Declare sampler
sampler = Sampler(env, args.horizon)
# Run episodes
running_reward = deque(maxlen=100)
best_reward = None
for i_episode in trange(0, args.episodes, args.eb, desc="Episodes", unit_scale=args.eb):
# Sample trajectories
trajectories, losses_and_info = sampler.sample(args.eb, vape, render=False)#(i_episode%args.render_interval==0))
reco_loss, norm_loss, total_loss, added_to_batch, avoidance_score = zip(*losses_and_info)
# Update policy
finish_episode(trajectories, vape, args)
# Get quantities for summaries
episode_rewards = np.sum(trajectories['rewards'], axis=1)
mean_reward = np.mean(episode_rewards)
episode_lens = np.sum(trajectories['mask'], axis=1)
for sub_i in range(args.eb):
# Summaries: mean episode reward for 100 episodes
running_reward.append(episode_rewards[sub_i])
writer.add_scalar('data/mean_100episode_reward', np.mean(running_reward), i_episode + sub_i)
# Summaries: mean episode len
writer.add_scalar('data/episode_len', episode_lens[sub_i], i_episode + sub_i)
writer.add_scalar('data/episode_reward', episode_rewards[sub_i], i_episode + sub_i)
writer.add_scalar('data/added_to_batch', np.sum(added_to_batch), i_episode/args.eb)
writer.add_scalar('data/mean_avoidance', np.mean(avoidance_score), i_episode/args.eb)
writer.add_scalar('data/reco_loss', np.mean(reco_loss), i_episode/args.eb)
writer.add_scalar('data/norm_loss', np.mean(norm_loss), i_episode/args.eb)
# Save best model if needed
if (best_reward is None) or (mean_reward > best_reward):
best_reward = mean_reward
print("Saving best model:", best_reward)
torch.save(vape.state_dict(), checkpoint_best_filename)
# Check if completed
if np.mean(running_reward) > env.spec.reward_threshold:
print("Solved, stopping. Mean reward:", np.mean(running_reward))
break
# Save final model
torch.save(vape.state_dict(), checkpoint_final_filename)
# Close env and writer
env.close()
writer.close()
