default=True,
help='Image-based states')
parser.add_argument('--n-steps',
type=int,
default=500000,
help='number of steps for training')
parser.add_argument('--name', help='run name (within the experiment)')
parser.add_argument('--experiment-name', help='experiment name')
args = parser.parse_args()
env = KukaEnv(renders=args.render,
is_discrete=True,
max_steps=args.max_ep_len,
action_repeat=args.repeat,
images=False,
static_all=True,
static_obj_rnd_pos=False,
rnd_obj_rnd_pos=False,
full_color=False,
width=84,
height=84)
env.seed(args.seed)
torch.manual_seed(args.seed)
np.random.seed(args.seed)
env = FrameStackEnv(env, 3, 'tensors', 'states')
experience_generator = ExperienceGenerator(env)
agent = PPOAgentStates(num_layers=3,
parser.add_argument('--episodes',
type=int,
default=10000,
help='number of training episodes to run')
parser.add_argument('--images',
action='store_true',
default=False,
help='Image-based states')
parser.add_argument('--name', help='experiment name')
args = parser.parse_args()
env = KukaEnv(renders=args.render,
is_discrete=True,
max_steps=args.max_ep_len,
action_repeat=args.repeat,
images=args.images,
static_all=True,
static_obj_rnd_pos=False,
rnd_obj_rnd_pos=False,
full_color=False)
env.seed(args.seed)
torch.manual_seed(args.seed)
saved_action = namedtuple('saved_action', ['log_prob', 'value'])
policy_net = DQNCnn(7)
target_net = DQNCnn(7)
memory = ReplayBuffer(100000, 4)
default=True,
help='Image-based states')
parser.add_argument('--n-steps',
type=int,
default=100000,
help='number of steps for training')
parser.add_argument('--name', help='experiment name')
parser.add_argument('--experiment-name', help='experiment name')
args = parser.parse_args()
env = KukaEnv(renders=args.render,
is_discrete=True,
max_steps=args.max_ep_len,
action_repeat=args.repeat,
images=True,
static_all=False,
static_obj_rnd_pos=True,
rnd_obj_rnd_pos=False,
full_color=True,
width=84,
height=84)
# env = gym.make('BowlingDeterministic')
env.seed(args.seed)
torch.manual_seed(args.seed)
np.random.seed(args.seed)
env = FrameStackEnv(env, 3, 'tensors')
agent = SACAgentImages(
from environments.kuka import KukaEnv
import matplotlib.pyplot as plt
import numpy as np
import pybullet as p
import time
from PIL import Image
from models.encoders import VAE
import torch
import torch.nn.functional as F
from torchvision import transforms
from tqdm import tqdm
env = KukaEnv(images=True,
static_all=False,
is_discrete=True,
static_obj_rnd_pos=False,
rnd_obj_rnd_pos=False,
renders=False,
full_color=True)
vae = VAE(32)
vae.to('cuda:0')
optimizer = torch.optim.Adam(vae.parameters(), lr=0.0002)
def vae_loss(x, x_hat, mu, var, weight):
# Reconstruction error
recon_err = F.binary_cross_entropy(x_hat, x, reduction='sum')
# KL