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Interpretable policies for reinforcement learning by empirical fuzzy sets
سیاست های قابل تفسیر برای یادگیری تقویتی توسط مجموعه های فازی تجربی-2020 This paper proposes a method and an algorithm to implement interpretable fuzzy reinforcement learning
(IFRL). It provides alternative solutions to common problems in RL, like function approximation and continuous
action space. The learning process resembles that of human beings by clustering the encountered states,
developing experiences for each of the typical cases, and making decisions fuzzily. The learned policy can
be expressed as human-intelligible IF-THEN rules, which facilitates further investigation and improvement. It
adopts the actor–critic architecture whereas being different from mainstream policy gradient methods. The
value function is approximated through the fuzzy system AnYa. The state–action space is discretized into a
static grid with nodes. Each node is treated as one prototype and corresponds to one fuzzy rule, with the value
of the node being the consequent. Values of consequents are updated using the Sarsa(????) algorithm. Probability
distribution of optimal actions regarding different states is estimated through Empirical Data Analytics (EDA),
Autonomous Learning Multi-Model Systems (ALMMo), and Empirical Fuzzy Sets (εFS). The fuzzy kernel of
IFRL avoids the lack of interpretability in other methods based on neural networks. Simulation results with
four problems, namely Mountain Car, Continuous Gridworld, Pendulum Position, and Tank Level Control, are
presented as a proof of the proposed concept. Keywords: Interpretable fuzzy systems | Reinforcement learning | Probability distribution learning | Autonomous learning systems | AnYa type fuzzy systems | Empirical Fuzzy Sets |
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