temporal difference learning
时间: 2023-04-12 14:03:37 浏览: 87
时序差分学习(Temporal Difference Learning)是一种强化学习算法,它通过比较当前状态下的估计值和下一个状态的估计值来更新价值函数。这种方法可以在不需要完整的环境模型的情况下进行学习,因此被广泛应用于机器人控制、游戏智能等领域。
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time difference learning
Time difference learning is a type of reinforcement learning method in which an agent learns from the differences between predicted and actual outcomes over time. This approach is based on the idea that the agent can update its predictions based on the temporal difference between the expected and actual rewards it receives.
The time difference learning algorithm is commonly used in the context of Markov decision processes (MDPs) and is particularly useful for problems with delayed rewards. In these cases, the agent must learn to balance immediate rewards with long-term goals, which can be challenging without a mechanism for temporal difference learning.
Overall, time difference learning is a powerful tool for developing reinforcement learning algorithms that can learn from experience over time and make informed decisions based on past outcomes.
reinforcement learning sutton .pdf
《强化学习:理论与算法》是一本由Richard S. Sutton和Andrew G. Barto联合撰写的经典教材。该教材深入介绍了强化学习的理论和算法,并成为该领域的重要参考资料。
强化学习是一种机器学习方法,旨在让智能系统通过与环境的交互来学习最佳策略。该方法侧重于通过试错学习来优化决策过程,从而实现智能系统的自主学习和决策能力。在强化学习中,智能系统通过与环境的不断交互,观察当前状态并基于奖励信号采取行动,目标是最大化累积奖励。
《强化学习:理论与算法》详细介绍了强化学习的基本概念和数学模型,例如马尔可夫决策过程(Markov Decision Process, MDP)和贝尔曼方程(Bellman Equation)等。随后,书中介绍了一些重要的强化学习算法,如动态规划(Dynamic Programming)、蒙特卡洛方法(Monte Carlo Methods)、时间差分学习(Temporal Difference Learning)和Q学习(Q-Learning)等。
此外,《强化学习:理论与算法》还介绍了连续动作空间、部分可观测马尔可夫决策过程(POMDP)以及函数逼近等高级主题。该书以清晰的语言和丰富的示例,帮助读者理解和应用强化学习算法。
总的来说,《强化学习:理论与算法》是一本权威且具有影响力的教材,为强化学习领域的研究人员和学习爱好者提供了宝贵的学习资源。