class dqn():
def __init__(self, env, policy):
self.N = env.map.get_node_num()
self.env = env
config_path = join(dirname(policy), "default.json")
args, _ = load_args(config_path)
# model
state_shape = env.observation_space.shape or env.observation_space.n
action_shape = env.action_space.shape or env.action_space.n
self.net = Net(args.layer_num, state_shape,
action_shape, args.device, # dueling=(1, 1)
).to(args.device)
state_dict = torch.load(policy)
self.net.load_state_dict(state_dict)
def next_node(self, obs):
out, _ = self.net(np.array([obs]))
out = out.detach().cpu()[0]
return np.argmax(out)
'.')[0] + '_camera' + '.world'
state_dict = {}
state_dict_raw = torch.load(model_path)
for key in state_dict_raw.keys():
if key.split('.')[0] == 'model':
state_dict[key[6:]] = state_dict_raw[key]
env = wrapper_dict[wrapper_config['wrapper']](gym.make('jackal_navigation-v0',
**env_config),
wrapper_config['wrapper_args'])
state_shape = env.observation_space.shape or env.observation_space.n
action_shape = env.action_space.shape or env.action_space.n
model = Net(training_config['layer_num'], state_shape, action_shape,
config['device']).to(config['device'])
model.load_state_dict(state_dict)
range_dict = {
'max_vel_x': [0.1, 2],
'max_vel_theta': [0.314, 3.14],
'vx_samples': [1, 12],
'vtheta_samples': [1, 40],
'path_distance_bias': [0.1, 1.5],
'goal_distance_bias': [0.1, 2]
}
rs = []
cs = []
pms = np.array(env_config['param_init'])
pms = np.expand_dims(pms, -1)
succeed = 0
