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Reinforcement learning (RL) is an area of machine learning concerned with how intelligent agents ought to take actions in an environment in order to maximize the notion of cumulative reward. Reinforcement learning is one of three basic machine learning paradigms, alongside supervised learning and … See more Due to its generality, reinforcement learning is studied in many disciplines, such as game theory, control theory, operations research, information theory, simulation-based optimization, multi-agent systems See more The exploration vs. exploitation trade-off has been most thoroughly studied through the multi-armed bandit problem and for finite state space MDPs in Burnetas and Katehakis (1997). Reinforcement learning requires clever exploration … See more Research topics include: • actor-critic • adaptive methods that work with fewer (or no) parameters under a large number of conditions See more • Temporal difference learning • Q-learning • State–action–reward–state–action (SARSA) See more Even if the issue of exploration is disregarded and even if the state was observable (assumed hereafter), the problem remains to use past experience to find out which … See more Both the asymptotic and finite-sample behaviors of most algorithms are well understood. Algorithms with provably good online … See more Associative reinforcement learning Associative reinforcement learning tasks combine facets of stochastic learning automata tasks and … See more WebApr 2, 2024 · In Supervised learning, the decision is made on the initial input or the input given at the start: In Reinforcement learning decision is dependent, So we give labels to sequences of dependent decisions: In … Web马尔可夫决策过程（Markov Decision Processes,MDPs）. MDPs 简单说就是一个智能体（Agent）采取行动（Action）从而改变自己的状态（State）获得奖励（Reward）与环 … oversize pantalon
