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Python logger.push_prefix方法代码示例

本文整理汇总了Python中rllab.misc.logger.push_prefix方法的典型用法代码示例。如果您正苦于以下问题:Python logger.push_prefix方法的具体用法?Python logger.push_prefix怎么用?Python logger.push_prefix使用的例子?那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在rllab.misc.logger的用法示例。


在下文中一共展示了logger.push_prefix方法的3个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。

示例1: train

# 需要导入模块: from rllab.misc import logger [as 别名]
# 或者: from rllab.misc.logger import push_prefix [as 别名]
def train(self):

        memory = ReplayMem(
            obs_dim=self.env.observation_space.flat_dim,
            act_dim=self.env.action_space.flat_dim,
            memory_size=self.memory_size)

        itr = 0
        path_length = 0
        path_return = 0
        end = False
        obs = self.env.reset()

        for epoch in range(self.n_epochs):
            logger.push_prefix("epoch #%d | " % epoch)
            logger.log("Training started")
            for epoch_itr in pyprind.prog_bar(range(self.epoch_length)):
                # run the policy
                if end:
                    # reset the environment and stretegy when an episode ends
                    obs = self.env.reset()
                    self.strategy.reset()
                    # self.policy.reset()
                    self.strategy_path_returns.append(path_return)
                    path_length = 0
                    path_return = 0
                # note action is sampled from the policy not the target policy
                act = self.strategy.get_action(obs, self.policy)
                nxt, rwd, end, _ = self.env.step(act)

                path_length += 1
                path_return += rwd

                if not end and path_length >= self.max_path_length:
                    end = True
                    if self.include_horizon_terminal:
                        memory.add_sample(obs, act, rwd, end)
                else:
                    memory.add_sample(obs, act, rwd, end)

                obs = nxt

                if memory.size >= self.memory_start_size:
                    for update_time in range(self.n_updates_per_sample):
                        batch = memory.get_batch(self.batch_size)
                        self.do_update(itr, batch)

                itr += 1

            logger.log("Training finished")
            if memory.size >= self.memory_start_size:
                self.evaluate(epoch, memory)
            logger.dump_tabular(with_prefix=False)
            logger.pop_prefix()

        # self.env.terminate()
        # self.policy.terminate() 
开发者ID:awslabs,项目名称:dynamic-training-with-apache-mxnet-on-aws,代码行数:59,代码来源:ddpg.py

示例2: setup

# 需要导入模块: from rllab.misc import logger [as 别名]
# 或者: from rllab.misc.logger import push_prefix [as 别名]
def setup(self, env, policy, start_itr):

        if not self.args.algo == 'thddpg':
            # Baseline
            if self.args.baseline_type == 'linear':
                baseline = LinearFeatureBaseline(env_spec=env.spec)
            elif self.args.baseline_type == 'zero':
                baseline = ZeroBaseline(env_spec=env.spec)
            else:
                raise NotImplementedError(self.args.baseline_type)

            if self.args.control == 'concurrent':
                baseline = [baseline for _ in range(len(env.agents))]
        # Logger
        default_log_dir = config.LOG_DIR
        if self.args.log_dir is None:
            log_dir = osp.join(default_log_dir, self.args.exp_name)
        else:
            log_dir = self.args.log_dir

        tabular_log_file = osp.join(log_dir, self.args.tabular_log_file)
        text_log_file = osp.join(log_dir, self.args.text_log_file)
        params_log_file = osp.join(log_dir, self.args.params_log_file)

        logger.log_parameters_lite(params_log_file, self.args)
        logger.add_text_output(text_log_file)
        logger.add_tabular_output(tabular_log_file)
        prev_snapshot_dir = logger.get_snapshot_dir()
        prev_mode = logger.get_snapshot_mode()
        logger.set_snapshot_dir(log_dir)
        logger.set_snapshot_mode(self.args.snapshot_mode)
        logger.set_log_tabular_only(self.args.log_tabular_only)
        logger.push_prefix("[%s] " % self.args.exp_name)

        if self.args.algo == 'tftrpo':
            algo = MATRPO(env=env, policy_or_policies=policy, baseline_or_baselines=baseline,
                          batch_size=self.args.batch_size, start_itr=start_itr,
                          max_path_length=self.args.max_path_length, n_itr=self.args.n_iter,
                          discount=self.args.discount, gae_lambda=self.args.gae_lambda,
                          step_size=self.args.step_size, optimizer=ConjugateGradientOptimizer(
                              hvp_approach=FiniteDifferenceHvp(base_eps=1e-5)) if
                          self.args.recurrent else None, ma_mode=self.args.control)
        elif self.args.algo == 'thddpg':
            qfunc = thContinuousMLPQFunction(env_spec=env.spec)
            if self.args.exp_strategy == 'ou':
                es = OUStrategy(env_spec=env.spec)
            elif self.args.exp_strategy == 'gauss':
                es = GaussianStrategy(env_spec=env.spec)
            else:
                raise NotImplementedError()

            algo = thDDPG(env=env, policy=policy, qf=qfunc, es=es, batch_size=self.args.batch_size,
                          max_path_length=self.args.max_path_length,
                          epoch_length=self.args.epoch_length,
                          min_pool_size=self.args.min_pool_size,
                          replay_pool_size=self.args.replay_pool_size, n_epochs=self.args.n_iter,
                          discount=self.args.discount, scale_reward=0.01,
                          qf_learning_rate=self.args.qfunc_lr,
                          policy_learning_rate=self.args.policy_lr,
                          eval_samples=self.args.eval_samples, mode=self.args.control)
        return algo 
开发者ID:sisl,项目名称:MADRL,代码行数:63,代码来源:rurllab.py

示例3: train

# 需要导入模块: from rllab.misc import logger [as 别名]
# 或者: from rllab.misc.logger import push_prefix [as 别名]
def train(self):

        memory = ReplayMem(
            obs_dim=self.env.observation_space.flat_dim,
            act_dim=self.env.action_space.flat_dim,
            memory_size=self.memory_size)

        itr = 0
        path_length = 0
        path_return = 0
        end = False
        obs = self.env.reset()

        for epoch in xrange(self.n_epochs):
            logger.push_prefix("epoch #%d | " % epoch)
            logger.log("Training started")
            for epoch_itr in pyprind.prog_bar(range(self.epoch_length)):
                # run the policy
                if end:
                    # reset the environment and stretegy when an episode ends
                    obs = self.env.reset()
                    self.strategy.reset()
                    # self.policy.reset()
                    self.strategy_path_returns.append(path_return)
                    path_length = 0
                    path_return = 0
                # note action is sampled from the policy not the target policy
                act = self.strategy.get_action(obs, self.policy)
                nxt, rwd, end, _ = self.env.step(act)

                path_length += 1
                path_return += rwd

                if not end and path_length >= self.max_path_length:
                    end = True
                    if self.include_horizon_terminal:
                        memory.add_sample(obs, act, rwd, end)
                else:
                    memory.add_sample(obs, act, rwd, end)

                obs = nxt

                if memory.size >= self.memory_start_size:
                    for update_time in xrange(self.n_updates_per_sample):
                        batch = memory.get_batch(self.batch_size)
                        self.do_update(itr, batch)

                itr += 1

            logger.log("Training finished")
            if memory.size >= self.memory_start_size:
                self.evaluate(epoch, memory)
            logger.dump_tabular(with_prefix=False)
            logger.pop_prefix()

        # self.env.terminate()
        # self.policy.terminate() 
开发者ID:mahyarnajibi,项目名称:SNIPER-mxnet,代码行数:59,代码来源:ddpg.py


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