China
Africa
Explore chapters and articles related to this topic
Expert Systems for Microgrids
Published in KTM Udayanga Hemapala, MK Perera, Smart Microgrid Systems, 2023
KTM Udayanga Hemapala, MK Perera
Therefore, discussions have been held on making full use of the MDP structure in a more efficient manner. As a result, the temporal difference learning approach has been introduced. Unlike dynamic programming approaches, temporal difference learning is a model-free learning method where an environmental model is not required. As in the MC approach the agents learn directly from their experience. However, TD learning promotes online learning without waiting until the final outcome is available. According to equation (6.8), at each time step the TD estimations of the state value functions are updated. This an efficient use of the MDP structure. V(st)←V(st)+α[Rt+1+γV(st+1)−V(st)]
Learning under Random Updates
Published in Hamidou Tembine, Distributed Strategic Learning for Wireless Engineers, 2018
In the single player case, the approximation technique used in temporal difference learning (TD-learning) reduces the curse of dimensionality by finding a parameterized approximated solution within a prescribed finite-dimensional function class. Using stochastic approximation techniques, one can show that the TD-learning that approximates the value functions converges almost surely under suitable conditions. We describe a discounted stochastic game [144, 168] by Γδ=(N,W,Aj,R˜j(w,a),q,δ) where WA={(w,a)|(w,a)∈W,aj′∈Aj′(w)}R˜j:(w,a)∈WA→ℝ,δ ∈ (0,1)q:WA→Δ(W).
Learning adversarial policy in multiple scenes environment via multi-agent reinforcement learning
Published in Connection Science, 2021
Yang Li, Xinzhi Wang, Wei Wang, Zhenyu Zhang, Jianshu Wang, Xiangfeng Luo, Shaorong Xie
Policy gradient methods are realised by combining policy gradient and stochastic gradient ascent algorithms, which have been used for many tasks. The gradient of the policy can be written as where denotes the state distribution. The improved algorithms of policy gradient method differ primarily in the way of estimating . Some methods learn an approximation of true action-value function such as temporal-difference learning (Sutton & Barto, 2018). Some methods simply use a sample return , such as the REINFORCE algorithm (Williams, 1992). is often called as the critic and a variety of actor-critic algorithms (Sutton & Barto, 2018) are proposed.
Wireless Network Design Optimization for Computer Teaching with Deep Reinforcement Learning Application
Published in Applied Artificial Intelligence, 2023
Q-learning is known as off-policy temporal difference learning. Different from the temporal difference learning algorithm, Q-learning is a model-based dynamic programming algorithm, so it is necessary to examine the potential reward of each behavior in each learning process of the subject to ensure that the learning process converges. The optimal reward discount sum and Q value update iteration in the Q-learning algorithm are:
A tutorial introduction to reinforcement learning
Published in SICE Journal of Control, Measurement, and System Integration, 2023
In principle, Theorem 2.3 can be used to compute, to arbitrary precision, the value vector of a Markov reward process. Similarly, Theorem 3.7 can be used to compute, to arbitrary precision, the optimal action-value function of a MDP, from which both the optimal value function and the optimal policy can be determined. However, both theorems depend crucially on knowing the dynamics of the underlying process. For instance, if the state transition matrix A is not known, it would not be possible to carry out the iterations Early researchers in RL were aware of this issue and developed several algorithms that do not require explicit knowledge of the dynamics of the underlying process. Instead, it is assumed that a sample path of the Markov process, together with the associated reward process, are available for use. With this information, one can think of two distinct approaches. First, one can use the sample path to estimate the state transition matrix, call it . After a sufficiently long sample path has been observed, the contraction iteration above can be applied with A replaced by . This would correspond to so-called “indirect adaptive control.” The second approach would be to use the sample path right from time t = 0, and adjust only one component of the estimated value function at each time instant t. This would correspond to so-called “direct adaptive control.” Using a similar approach, it is also possible to estimate the action-value function based on a single sample path. We describe two such algorithms, namely temporal difference learning for estimating the value function of a Markov reward process and Q-learning for estimating the action-value function of a MDP. Within temporal difference, we make a further distinction between estimating the full value vector and estimating a projection of the value vector onto a lower-dimensional subspace.