def load_model_ddpg(model_path, user_embeddings_path, item_embeddings_path,
input_dim, action_dim, hidden_size, device):
with open(user_embeddings_path, "rb") as f:
user_embeddings = np.load(f)
with open(item_embeddings_path, "rb") as f:
item_embeddings = np.load(f)
model = Actor(input_dim, action_dim, hidden_size, user_embeddings,
item_embeddings)
model.load_state_dict(torch.load(model_path, map_location=device))
model.eval()
return model
def test(args=get_args()):
env = QuadcopterEnv()
model = Actor(None, env.observation_space.shape, env.action_space.shape,
[-1, 1], args.device).to(args.device)
args.model_path = os.path.join(args.logdir, 'lqr')
model.load_state_dict(
torch.load(os.path.join(args.model_path, 'policy.pth'),
map_location=args.device))
for i in range(10):
obs = env.reset()
env.render()
done = False
while not done:
act = model(obs.reshape((1, -1)))[0].detach().cpu().numpy()[0]
obs, reward, done, info = env.step(act)
env.render()
def test(args=get_args()):
env = DubinEnv()
# env.set_obs([])
model = Actor(None, env.observation_space['dynamics'].shape,
env.action_space.shape, [-1, 1], args.device).to(args.device)
args.model_path = os.path.join(args.logdir, 'lqr')
model.load_state_dict(
torch.load(os.path.join(args.model_path, 'policy.pth'),
map_location=args.device))
for i in range(10):
env.reset()
# env.state[:2] -= env.goal[:2]
# env.goal[:2] -= env.goal[:2]
obs = env._obs()
env.render()
done = False
while not done:
normed_obs = obs['dynamics'].reshape((1, -1))
# /np.array([20,20,np.pi,1,np.pi])
act = model(normed_obs)[0].detach().cpu().numpy()[0]
obs, reward, done, info = env.step(act)
env.render()
