Research Article
A Sentence-Level Joint Relation Classification Model Based on Reinforcement Learning
Algorithm 1
Joint training of the RL model and joint network model.
| Input: Number of Episode N. Training data X, Initialize the RL model parameters and joint network model Parameters | | Output: RL model parameters ψ and joint network model Parameters θ | (1) | for episode n = 1 to N do | (2) | foreachdo | (3) | Calculate the predicted score for each state | (4) | According to the predicted score, the action taken on the state is obtained | (5) | Calculate temporary and average Awards | (6) | Update the parameters of RL model | (7) | Calculate total award | (8) | end foreach | (9) | Train and update the parameters θ of joint network model | (10) | Update the parameters of RL model | (11) | Find the best parameters for RL model according to the reward | (12) | Update the weights of the RL networks | (13) | end for |
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