def test_rollout_gen(self):
env = gym.make('SpaceInvaders-v4')
models = config.basepath() / 'SpaceInvaders-v4' / 'models'
visualsfile = models / 'GM53H301W5YS38XH'
visuals = Storeable.load(str(visualsfile)).to(config.device())
controllerfile = models / 'best_model68'
controller = torch.load(str(controllerfile))
policy = VCPolicy(visuals, controller)
viewer = ImageViewer('screen', (420, 360), 'numpyRGB')
for screen, observation, reward, done, info, action in RolloutGen(
env, policy):
viewer.update(screen)
select = tf.SelectChannels([3, 4, 5, 6])
observe = tf.ViewChannels('transform', (320, 480), channels=[0, 1, 2])
segmentor = TVT.Compose([
shots, player, cut_player, invader, cut_invader, barrier, select,
TVT.ToTensor(),
tf.CoordConv()
])
env = gym.make('SpaceInvaders-v4')
env = gym_wrappers.StepReward(env, step_reward=1)
policy_nets = []
cma_file = config.basepath(
) / 'SpaceInvaders-v4' / 'policy_runs' / '603' / 'cma_8'
if cma_file.exists():
with Path(cma_file).open('rb') as f:
cma = pickle.load(f)
else:
cma = CMA()
episode_steps = []
sample_size = 1000
epochs = 200
rollouts = 1
z_size = 32