def make_object_under_test(self, renderer):
self.environment = environment.Environment(
task=tasks.NoReward(),
action_space=action_spaces.SelectMove(),
renderers={'obs': renderer},
init_sprites=lambda: [sprite.Sprite(c0=255)],
max_episode_length=7)
def make_object_under_test(self):
"""Environment creator used by test_utils.EnvironmentTestMixin."""
env = environment.Environment(
task=tasks.NoReward(),
action_space=action_spaces.SelectMove(),
renderers={},
init_sprites=lambda: [sprite.Sprite(c0=255)],
max_episode_length=7)
return env
def testConfig(self, task_module, modes=('train', 'test'), replicas=3):
for mode in modes:
print(mode)
for _ in range(replicas):
config = task_module.get_config(mode=mode)
config['renderers'] = {}
env = environment.Environment(**config)
env.observation_spec()
action = env.action_space.sample()
num_episodes = 0
step = env.reset()
while num_episodes < 5:
if step.first():
num_episodes += 1
step = env.step(action)
def main(argv):
del argv
config = importlib.import_module(FLAGS.config)
config = config.get_config(FLAGS.mode)
config['renderers']['success'] = renderers.Success() # Used for logging
env = environment.Environment(**config)
agent = RandomAgent(env)
# Loop over episodes, logging success and mean reward per episode
for episode in range(FLAGS.num_episodes):
timestep = env.reset()
rewards = []
while not timestep.last():
action = agent.step(timestep)
timestep = env.step(action)
rewards.append(timestep.reward)
logging.info('Episode %d: Success = %r, Reward = %s.', episode,
timestep.observation['success'], np.nanmean(rewards))
def setup_run_ui(env_config, render_size, task_hsv_colors, anti_aliasing):
"""Start a Demo UI given an env_config."""
if isinstance(env_config['action_space'], action_spaces.SelectMove):
# DragAndDrop is a bit easier to demo than the SelectMove action space
#env_config['action_space'] = action_spaces.DragAndDrop(scale=0.5, noise_scale=np.array([0,0,0.025,0.025]), proportional_motion_noise=0.35)
env_config['action_space'] = action_spaces.DragAndDrop(
scale=0.5,
noise_scale=0.02,
proportional_motion_noise=0.35,
filter_distribs=env_config['metadata']['filter_distribs'])
agent = HumanDragAndDropAgent(env_config['action_space'])
elif isinstance(env_config['action_space'], action_spaces.Embodied):
agent = HumanEmbodiedAgent(env_config['action_space'])
else:
raise ValueError(
'Demo is not configured to run with action space {}.'.format(
env_config['action_space']))
env_config['renderers'] = {
'image':
renderers.PILRenderer(image_size=(render_size, render_size),
color_to_rgb=renderers.color_maps.hsv_to_rgb
if task_hsv_colors else None,
anti_aliasing=anti_aliasing),
'success':
renderers.Success()
}
env = environment.Environment(**env_config)
ui = MatplotlibUI()
agent.register_callbacks(ui)
# Start RL loop
timestep = env.reset()
ui.update(timestep, action=None)
while True:
action = agent.step(timestep)
timestep = env.step(action)
if isinstance(env_config['action_space'], action_spaces.DragAndDrop):
ui.update(timestep, action)
else:
ui.update(timestep, None)
def main(_):
config = importlib.import_module(FLAGS.config)
config = config.get_config(FLAGS.mode)
if isinstance(config['action_space'], action_spaces.SelectMove):
# DragAndDrop is a bit easier to demo than the SelectMove action space
config['action_space'] = action_spaces.DragAndDrop(scale=0.5)
agent = HumanDragAndDropAgent(config['action_space'])
elif isinstance(config['action_space'], action_spaces.Embodied):
agent = HumanEmbodiedAgent(config['action_space'])
else:
raise ValueError(
'Demo is not configured to run with action space {}.'.format(
config['action_space']))
config['renderers'] = {
'image':
renderers.PILRenderer(image_size=(FLAGS.render_size,
FLAGS.render_size),
color_to_rgb=color_maps.hsv_to_rgb
if FLAGS.task_hsv_colors else None,
anti_aliasing=FLAGS.anti_aliasing),
'success':
renderers.Success()
}
env = environment.Environment(**config)
demo = DemoUI()
for event_name, callback in agent.callbacks().items():
demo.register_callback(event_name, callback)
timestep = env.reset()
demo.update(timestep, action=None)
while True:
action = agent.step(timestep)
timestep = env.step(action)
if isinstance(config['action_space'], action_spaces.DragAndDrop):
demo.update(timestep, action)
else:
demo.update(timestep, None)
def testTaskTermination(self):
task = tasks.FindGoalPosition(goal_position=(0.5, 0.5))
action_space = action_spaces.SelectMove()
env_renderers = {}
init_sprites = lambda: [sprite.Sprite(x=0.25, y=0.25, c0=255)]
env = environment.Environment(task, action_space, env_renderers,
init_sprites)
donothing_action = np.array([0.25, 0.25, 0.5, 0.5])
success_action = np.array([0.25, 0.25, 0.75, 0.75])
timestep = env.step(donothing_action)
self.assertTrue(timestep.first())
timestep = env.step(donothing_action)
self.assertTrue(timestep.mid())
timestep = env.step(success_action)
self.assertTrue(timestep.last())
timestep = env.step(success_action)
self.assertTrue(timestep.first())
def setup_run_ui(env_config, render_size, task_hsv_colors, anti_aliasing):
"""Start a Demo UI given an env_config."""
if isinstance(env_config['action_space'], action_spaces.SelectMove):
# DragAndDrop is a bit easier to demo than the SelectMove action space
env_config['action_space'] = action_spaces.DragAndDrop(scale=0.5)
agent = HumanDragAndDropAgent(env_config['action_space'])
elif isinstance(env_config['action_space'], action_spaces.Embodied):
agent = HumanEmbodiedAgent(env_config['action_space'])
else:
raise ValueError(
'Demo is not configured to run with action space {}.'.format(
env_config['action_space']))
env_config['renderers'] = {
'image':
renderers.PILRenderer(
image_size=(render_size, render_size),
color_to_rgb=renderers.color_maps.hsv_to_rgb
if task_hsv_colors else None,
anti_aliasing=anti_aliasing),
'success':
renderers.Success()
}
env = environment.Environment(**env_config)
demo = DemoUI()
for event_name, callback in agent.callbacks().items():
demo.register_callback(event_name, callback)
# Start RL loop
timestep = env.reset()
demo.update(timestep, action=None)
while True:
action = agent.step(timestep)
timestep = env.step(action)
if isinstance(env_config['action_space'], action_spaces.DragAndDrop):
demo.update(timestep, action)
else:
demo.update(timestep, None)
def main(argv):
del argv
config = importlib.import_module(FLAGS.config)
config = config.get_config(FLAGS.mode)
config['renderers']['success'] = renderers.Success() # Used for logging
env = environment.Environment(**config)
agent = RandomAgent(env)
episode_frames = np.zeros((20, 64, 64, 3))
# Loop over episodes, logging success and mean reward per episode
for episode in range(FLAGS.num_episodes):
timestep = env.reset()
t = 0
rewards = []
while not timestep.last():
episode_frames[t] = timestep.observation['image']
action = agent.step(timestep)
timestep = env.step(action)
rewards.append(timestep.reward)
t += 1
logging.info('Episode %d: Success = %r, Reward = %s.', episode,
timestep.observation['success'], np.nanmax(rewards))
np.save('random_actions', episode_frames)
