def __init__( self, goal=(1, 1), random_start=False, show_traces=True, completion_bonus=0., never_done=False, action_scale=1., ): Serializable.quick_init(self, locals()) Parameterized.__init__(self) self._goal = np.array(goal, dtype=np.float32) self._point = np.zeros(2) self._completion_bonus = completion_bonus self._never_done = never_done self._action_scale = action_scale self.screen = None self.screen_width = 500 self.screen_height = 500 self.zoom = 50. self.show_traces = show_traces self.random_start = random_start self._traces = deque(maxlen=MAX_SHOWN_TRACES)
def __init__(self, env=None, env_name=""): if env_name: super().__init__(gym.make(env_name)) else: super().__init__(env) Parameterized.__init__(self) Serializable.quick_init(self, locals())
def __init__(self, wrapped_env=None, wrapped_policy=None): assert isinstance(wrapped_policy, MultitaskPolicy) Serializable.quick_init(self, locals()) Parameterized.__init__(self) self._wrapped_env = wrapped_env self._wrapped_policy = wrapped_policy self._last_obs = None
def __init__(self, env=None, env_name=""): if env_name: super().__init__(gym.make(env_name)) else: super().__init__(env) self.action_space = self._to_akro_space(self.env.action_space) self.observation_space = self._to_akro_space( self.env.observation_space) Parameterized.__init__(self) Serializable.quick_init(self, locals())
def __init__(self, wrapped_env=None, wrapped_policy=None, latents=None, normalize=False): assert isinstance(wrapped_policy, MultitaskPolicy) Serializable.quick_init(self, locals()) Parameterized.__init__(self) self._wrapped_env = wrapped_env self._wrapped_policy = wrapped_policy self._latents = np.array(latents) self._last_obs = None self._normalize = normalize
def __init__(self, task_selection_strategy=round_robin, task_env_cls=None, task_args=None, task_kwargs=None): Serializable.quick_init(self, locals()) Parameterized.__init__(self) self._task_envs = [ task_env_cls(*t_args, **t_kwargs) for t_args, t_kwargs in zip(task_args, task_kwargs) ] self._task_selection_strategy = task_selection_strategy self._active_task = None
def __init__(self, wrapped_env=None, wrapped_policy=None, latents=None, skip_steps=1, deterministic=True): assert isinstance(wrapped_policy, MultitaskPolicy) assert isinstance(latents, list) Serializable.quick_init(self, locals()) Parameterized.__init__(self) self._wrapped_env = wrapped_env self._wrapped_policy = wrapped_policy self._latents = latents self._last_obs = None self._skip_steps = skip_steps self._deterministic = deterministic
def __init__(self, wrapped_env=None, wrapped_policy=None): assert isinstance(wrapped_policy, MultitaskPolicy) Serializable.quick_init(self, locals()) Parameterized.__init__(self) self._wrapped_env = wrapped_env self._wrapped_policy = wrapped_policy self._last_obs = None n_task = self._wrapped_policy.task_space.flat_dim one_hots = np.identity(n_task) latents, infos = self._wrapped_policy._embedding.get_latents(one_hots) latents_means = infos["mean"] self._latents_combination_hash = list() for i in range(n_task): for j in range(i + 1, n_task): self._latents_combination_hash.append( (latents_means[i, ...], latents_means[j, ...])) self._latents_combination_hash = tuple(self._latents_combination_hash) self._n_skills = n_task
def __init__(self, env_spec): Parameterized.__init__(self) self._env_spec = env_spec
def __init__(self, env): Serializable.quick_init(self, locals()) Parameterized.__init__(self) super().__init__(env)
def __init__(self, *args, **kwargs): Serializable.quick_init(self, locals()) Parameterized.__init__(self) super().__init__(SawyerReachXYZEnv(*args, **kwargs))
def __init__(self, env): Serializable.quick_init(self, locals()) Parameterized.__init__(self) NormalizedEnv.__init__(self, env)