强化学习是这种情况下的常用技术，而更多的传统情形下需要使用效用函数。
Reinforcement learning is a common technique for this scenario as well as the more traditional scenario of actually learning the utility function.
将强化学习技术运用于游戏的集中方法在文献里都有记载。
Several approaches applying reinforcement learning techniques to game playing have been described in the literature.
多代理体技术实现了教学的个性化，强化学习算法使得教学策略具有智能化。
Multi-Agent technology achieves the personalized in ITS, and reinforcement learning algorithm makes teaching strategies with the intelligent.
主要研究了强化学习算法及其在机器人足球比赛技术动作学习问题中的应用。
This paper discusses reinforcement learning(RL)algorithm and its application to technical action learning of soccer robot.
我认为这个例子就能说明强化学习技术可能还不够成熟，在这种奖励信号不够明确、约束条件太少的环境下，还不能真正有效地运行。
I think that's an example of where reinforcement learning is maybe not quite mature enough to really operate in these incredibly unconstrained environments where the reward signals are less crisp.
什么样的任务更适合应用强化学习技术？
What makes a task more appropriate for incorporating reinforcement learning?
通过强化学习技术，他们能够探索这18个或者更多个温控旋钮的最优设置，而这可能是连专门负责温控的工作人员都没有做过的。
Through reinforcement learning they were able to discover knob Settings for these 18 or however many knobs that weren't considered by the people doing that task.
通过强化学习技术，他们能够探索这18个或者更多个温控旋钮的最优设置，而这可能是连专门负责温控的工作人员都没有做过的。
Through reinforcement learning they were able to discover knob Settings for these 18 or however many knobs that weren't considered by the people doing that task.
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