这是由于Minimax-Q算法是一个对手独立算法(opponent-independent algorithm),不论对手策略是怎么样的,都收敛到该博弈的纳什均衡策略。就算对手采用一个非常弱的策略,当前智能体也不能学习到一个比纳什均衡策略更好的策略。 三、Nash Q-Learning算法 Nash Q-Learning算法是将Minimax-Q算法从零和博弈扩展到多人一般和...
We then derive a generalized minimax Q-learning algorithm, which computes the optimal policy when the model information is not known. Finally, we prove the convergence of the proposed generalized minimax Q-learning algorithm utilizing stochastic approximation techniques, under an assumption on the ...
Vanilla implementation of the paper's algorithm failed to yield any positive training result. Whereas the Minimax Q learning paper suggests learning to play the mixed policy which maximises the worst case reward, we have players play their Nash equilibrium strategy. For this reason we thought to ...
reinforcement-learning deep-reinforcement-learning q-learning artificial-intelligence neural-networks epsilon-greedy breadth-first-search alpha-beta-pruning depth-first-search minimax-algorithm policy-iteration value-iteration function-approximation expectimax particle-filter-tracking uniform-cost-search greedy-search...
minimax...在tic tac toe上的教程 How to make your Tic Tac Toe game unbeatable by using the minimax algorithm 链接:https...://medium.freecodecamp.org/how-to-make-your-tic-tac-toe-game-unbeatable-by-using-the-minimax-algorithm...麦肯锡关于AI应用场景的notes 链接:https://www.mckinsey.c...
Minimax Td-learning With Neural Nets In A Markov Game 作者: Dahl F.A.;Halck O.M.;摘要: A minimax version of temporal difference learning (minimax TDlearning) is given, similar to minimax Q-learning. The algorithm is used to train a neural net to play Campaign, a two-player zero-sum...
DES (数据加密标准 Data Encryption Standard)，因密钥长度过短及可能含有的后门引起争议，现也已经不在...
In this section, we present numerical simulations that demonstrate the performance of the pursuer group using the improved strategy obtained by the Deep Minimax Q-learning algorithm. Conclusion This paper focuses on the cooperative pursuit with multi-pursuer to capture a faster evader. The proposed su...
Minimax-Q learning is off-policy and greedy algorithm, whereas the QV and SARSA are on-policy algorithms. QV learning performs even better than SARSA as... S Singh,A Trivedi - IEEE 被引量: 12发表: 2012年 Adaptive approximation of monotone functions We study the classical problem of approxima...
A self-learning connect-4 game with GUI reinforcement-learningq-learningconnect-fourconnect-4minimax-agentq-learning-algorithm UpdatedJun 15, 2022 Python Tic Tac Toe Game in python with implemented minimax algorithm gamepythonminimaxminimax-agent ...