def populate_task(env, policy, scope=None): logger.log("Populating workers...") if singleton_pool.n_parallel > 1: singleton_pool.run_each( _worker_populate_task, [(pickle.dumps(env), pickle.dumps(policy), scope)] * singleton_pool.n_parallel ) else: # avoid unnecessary copying G = _get_scoped_G(singleton_pool.G, scope) G.env = env G.policy = policy logger.log("Populated")
def step(self, action_n): results = singleton_pool.run_each( worker_run_step, [(action_n, self.scope) for _ in self._alloc_env_ids], ) results = [x for x in results if x is not None] ids, obs, rewards, dones, env_infos = list(zip(*results)) ids = np.concatenate(ids) obs = self.observation_space.unflatten_n(np.concatenate(obs)) rewards = np.concatenate(rewards) dones = np.concatenate(dones) env_infos = tensor_utils.split_tensor_dict_list(tensor_utils.concat_tensor_dict_list(env_infos)) if env_infos is None: env_infos = [dict() for _ in range(self.num_envs)] items = list(zip(ids, obs, rewards, dones, env_infos)) items = sorted(items, key=lambda x: x[0]) ids, obs, rewards, dones, env_infos = list(zip(*items)) obs = list(obs) rewards = np.asarray(rewards) dones = np.asarray(dones) self.ts += 1 dones[self.ts >= self.max_path_length] = True reset_obs = self._run_reset(dones) for (i, done) in enumerate(dones): if done: obs[i] = reset_obs[i] self.ts[i] = 0 return obs, rewards, dones, tensor_utils.stack_tensor_dict_list(list(env_infos))
def __init__(self, env, n, max_path_length, scope=None): if scope is None: # initialize random scope scope = str(uuid.uuid4()) envs_per_worker = int(np.ceil(n * 1.0 / singleton_pool.n_parallel)) alloc_env_ids = [] rest_alloc = n start_id = 0 for _ in range(singleton_pool.n_parallel): n_allocs = min(envs_per_worker, rest_alloc) alloc_env_ids.append(list(range(start_id, start_id + n_allocs))) start_id += n_allocs rest_alloc = max(0, rest_alloc - envs_per_worker) singleton_pool.run_each(worker_init_envs, [(alloc, scope, env) for alloc in alloc_env_ids]) self._alloc_env_ids = alloc_env_ids self._action_space = env.action_space self._observation_space = env.observation_space self._num_envs = n self.scope = scope self.ts = np.zeros(n, dtype='int') self.max_path_length = max_path_length
def sample_paths( policy_params, max_samples, max_path_length=np.inf, env_params=None, scope=None, reset_arg=None, show_prog_bar=True, multi_task=False): """ :param policy_params: parameters for the policy. This will be updated on each worker process :param max_samples: desired maximum number of samples to be collected. The actual number of collected samples might be greater since all trajectories will be rolled out either until termination or until max_path_length is reached :param max_path_length: horizon / maximum length of a single trajectory :return: a list of collected paths """ if multi_task: assert len(policy_params) == singleton_pool.n_parallel all_params = [(params, scope) for params in policy_params] singleton_pool.run_each( _worker_set_policy_params, all_params, ) else: singleton_pool.run_each( _worker_set_policy_params, [(policy_params, scope)] * singleton_pool.n_parallel ) if env_params is not None: singleton_pool.run_each( _worker_set_env_params, [(env_params, scope)] * singleton_pool.n_parallel ) if multi_task: args = [(max_path_length, scope, arg) for arg in reset_arg] return singleton_pool.run_collect( _worker_collect_one_path, threshold=max_samples, args=args, show_prog_bar=show_prog_bar, multi_task=multi_task, ) else: return singleton_pool.run_collect( _worker_collect_one_path, threshold=max_samples, args=(max_path_length, scope, reset_arg), show_prog_bar=show_prog_bar, multi_task=multi_task, )
def _run_reset(self, dones): dones = np.asarray(dones) results = singleton_pool.run_each( worker_run_reset, [(dones, self.scope) for _ in self._alloc_env_ids], ) ids, flat_obs = list(map(np.concatenate, list(zip(*results)))) zipped = list(zip(ids, flat_obs)) sorted_obs = np.asarray([x[1] for x in sorted(zipped, key=lambda x: x[0])]) done_ids, = np.where(dones) done_flat_obs = sorted_obs[done_ids] done_unflat_obs = self.observation_space.unflatten_n(done_flat_obs) all_obs = [None] * self.num_envs done_cursor = 0 for idx, done in enumerate(dones): if done: all_obs[idx] = done_unflat_obs[done_cursor] done_cursor += 1 return all_obs
def start_worker(self): if singleton_pool.n_parallel > 1: singleton_pool.run_each(worker_init_tf) parallel_sampler.populate_task(self.algo.env, self.algo.policy) if singleton_pool.n_parallel > 1: singleton_pool.run_each(worker_init_tf_vars)
def set_seed(seed): singleton_pool.run_each( _worker_set_seed, [(seed + i,) for i in range(singleton_pool.n_parallel)] )
def terminate_task(scope=None): singleton_pool.run_each( _worker_terminate_task, [(scope,)] * singleton_pool.n_parallel )
def initialize(n_parallel): singleton_pool.initialize(n_parallel) singleton_pool.run_each(_worker_init, [(id,) for id in range(singleton_pool.n_parallel)])