def build_pend_env(args, **kwargs): alg = args.alg seed = args.seed flatten_dict_observations = alg not in {'her'} env = make_vec_env(args.env, 'classic_control', args.num_env or 1, seed, reward_scale=args.reward_scale, flatten_dict_observations=flatten_dict_observations) return VecVAEStack(env, k=k, load_from=vae_name.replace('/', ':'))
def build_pend_env(args, **kwargs): alg = args.alg seed = args.seed flatten_dict_observations = alg not in {'her'} env = make_vec_env(args.env, 'atari', args.num_env or 1, seed, reward_scale=args.reward_scale, flatten_dict_observations=flatten_dict_observations) return VecVAEStack(env, k=k, load_from=vae_name.replace('/', ':'), vae_network='atari', norm_fac=(1/255))
def build_pend_env(args, **kwargs): alg = args.alg seed = args.seed flatten_dict_observations = alg not in {'her'} env = make_vec_env(args.env, 'atari', args.num_env or 1, seed, reward_scale=args.reward_scale, flatten_dict_observations=flatten_dict_observations) return VecVAEStack(env, k=3, load_from=vae_name)
def build_pend_env(args, **kwargs): alg = args.alg seed = args.seed flatten_dict_observations = alg not in {'her'} env = make_vec_env(args.env, 'classic_control', args.num_env or 1, seed, reward_scale=args.reward_scale, flatten_dict_observations=flatten_dict_observations) return VecVAEStack( env, k=3, load_from='pendvisualuniform-b77.5-lat5-lr0.001-2019-03-21T00/13'. replace('/', ':'))
import numpy as np from baselines.common.cmd_util import make_vec_env from gym.envs.classic_control.pendulum_test import PendulumTestEnv import matplotlib.pyplot as plt import seaborn as sns vae_name = 'pendvisualuniform-b77.5-lat5-lr0.001-2019-03-21T00/13'.replace( '/', ':') env = make_vec_env('PendulumTest-v0', 'classic_control', 2, 0, flatten_dict_observations=False) venv = VecVAEStack(env, k=3, load_from=vae_name) thetas = np.linspace(0, 2 * np.pi, 20) dings1 = [] dings2 = [] o = venv.reset() d = False while not np.any(d): print(o) exit(0) # when using env obs, we can plot to sanity check theta # plt.imshow(np.squeeze(o), cmap='Greys_r', label='theta {}'.format(th)) # plt.title('{}'.format(th))