Ensemble-Based Deep Reinforcement Learning for Chatbots

Cuayáhuitl, Heriberto, Lee, Donghyeon, Ryu, Seonghan, Cho, Yongjin, Choi, Sungja, Indurthi, Satish, Yu, Seunghak, Choi, Hyungtak, Hwang, Inchul, Kim, Jihie

arXiv.org Artificial Intelligence 

Such an agent is typically characterised by: (i) a finite set of states 6 S {s i} that describe all possible situations in the environment; (ii) a finite set of actions A {a j} to change in the environment from one situation to another; (iii) a state transition function T (s,a,s null) that specifies the next state s null for having taken action a in the current state s; (iv) a reward function R (s,a,s null) that specifies a numerical value given to the agent for taking action a in state s and transitioning to state s null; and (v) a policy π: S A that defines a mapping from states to actions [2, 30]. The goal of a reinforcement learning agent is to find an optimal policy by maximising its cumulative discounted reward defined as Q (s,a) max π E[r t γr t 1 γ 2 r t 1 ... s t s,a t a,π ], where function Q represents the maximum sum of rewards r t discounted by factor γ at each time step. While a reinforcement learning agent takes actions with probability Pr ( a s) during training, it selects the best action at test time according to π (s) arg max a A Q (s,a). A deep reinforcement learning agent approximates Q using a multi-layer neural network [31]. The Q function is parameterised as Q(s,a; θ), where θ are the parameters or weights of the neural network (recurrent neural network in our case). Estimating these weights requires a dataset of learning experiences D {e 1,...e N} (also referred to as'experience replay memory'), where every experience is described as a tuple e t ( s t,a t,r t,s t 1). Inducing a Q function consists in applying Q-learning updates over minibatches of experience MB {( s,a,r,s null) U (D)} drawn uniformly at random from the full dataset D . This process is implemented in learning algorithms using Deep Q-Networks (DQN) such as those described in [31, 32, 33], and the following section describes a DQN-based algorithm for human-chatbot interaction.

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