Python baselines.common.tf_util.load_variables() Examples

def initialize(self, sess):
        self.sess = sess
        self.sess.run(tf.global_variables_initializer())
        self.save = functools.partial(save_variables, sess=self.sess)
        self.load = functools.partial(load_variables, sess=self.load)
        self.actor_optimizer.sync()
        self.critic_optimizer.sync()
        self.sess.run(self.target_init_updates) 

def runner(env, policy_func, load_model_path, timesteps_per_batch, number_trajs,
           stochastic_policy, save=False, reuse=False):

    # Setup network
    # ----------------------------------------
    ob_space = env.observation_space
    ac_space = env.action_space
    pi = policy_func("pi", ob_space, ac_space, reuse=reuse)
    U.initialize()
    # Prepare for rollouts
    # ----------------------------------------
    U.load_variables(load_model_path)

    obs_list = []
    acs_list = []
    len_list = []
    ret_list = []
    for _ in tqdm(range(number_trajs)):
        traj = traj_1_generator(pi, env, timesteps_per_batch, stochastic=stochastic_policy)
        obs, acs, ep_len, ep_ret = traj['ob'], traj['ac'], traj['ep_len'], traj['ep_ret']
        obs_list.append(obs)
        acs_list.append(acs)
        len_list.append(ep_len)
        ret_list.append(ep_ret)
    if stochastic_policy:
        print('stochastic policy:')
    else:
        print('deterministic policy:')
    if save:
        filename = load_model_path.split('/')[-1] + '.' + env.spec.id
        np.savez(filename, obs=np.array(obs_list), acs=np.array(acs_list),
                 lens=np.array(len_list), rets=np.array(ret_list))
    avg_len = sum(len_list)/len(len_list)
    avg_ret = sum(ret_list)/len(ret_list)
    print("Average length:", avg_len)
    print("Average return:", avg_ret)
    return avg_len, avg_ret


# Sample one trajectory (until trajectory end) 

def __init__(self, policy, env, nsteps,
            ent_coef=0.01, vf_coef=0.5, max_grad_norm=0.5, lr=7e-4,
            alpha=0.99, epsilon=1e-5, total_timesteps=int(80e6), lrschedule='linear'):

        sess = tf_util.get_session()
        nenvs = env.num_envs
        nbatch = nenvs*nsteps


        with tf.variable_scope('a2c_model', reuse=tf.AUTO_REUSE):
            step_model = policy(nenvs, 1, sess)
            train_model = policy(nbatch, nsteps, sess)

        A = tf.placeholder(train_model.action.dtype, train_model.action.shape)
        ADV = tf.placeholder(tf.float32, [nbatch])
        R = tf.placeholder(tf.float32, [nbatch])
        LR = tf.placeholder(tf.float32, [])

        neglogpac = train_model.pd.neglogp(A)
        entropy = tf.reduce_mean(train_model.pd.entropy())

        pg_loss = tf.reduce_mean(ADV * neglogpac)
        vf_loss = losses.mean_squared_error(tf.squeeze(train_model.vf), R)

        loss = pg_loss - entropy*ent_coef + vf_loss * vf_coef

        params = find_trainable_variables("a2c_model")
        grads = tf.gradients(loss, params)
        if max_grad_norm is not None:
            grads, grad_norm = tf.clip_by_global_norm(grads, max_grad_norm)
        grads = list(zip(grads, params))
        trainer = tf.train.RMSPropOptimizer(learning_rate=LR, decay=alpha, epsilon=epsilon)
        _train = trainer.apply_gradients(grads)

        lr = Scheduler(v=lr, nvalues=total_timesteps, schedule=lrschedule)

        def train(obs, states, rewards, masks, actions, values):
            advs = rewards - values
            for step in range(len(obs)):
                cur_lr = lr.value()

            td_map = {train_model.X:obs, A:actions, ADV:advs, R:rewards, LR:cur_lr}
            if states is not None:
                td_map[train_model.S] = states
                td_map[train_model.M] = masks
            policy_loss, value_loss, policy_entropy, _ = sess.run(
                [pg_loss, vf_loss, entropy, _train],
                td_map
            )
            return policy_loss, value_loss, policy_entropy


        self.train = train
        self.train_model = train_model
        self.step_model = step_model
        self.step = step_model.step
        self.value = step_model.value
        self.initial_state = step_model.initial_state
        self.save = functools.partial(tf_util.save_variables, sess=sess)
        self.load = functools.partial(tf_util.load_variables, sess=sess)
        tf.global_variables_initializer().run(session=sess)