I am trying to create a dialogue policy model on DSTC data. This model takes in a state of the conversation and outputs an act the machine must take. I am creating this model using reinforcement learning.

I have used this DQN Agent and a User Simulator to train the model with few modifications from the original code.

I am manually giving the rewards for each turn through long credit assignment with -1 at each turn and at end, (10 - turns) for success and -2*(max_turns) for failure. max_turns is kept at 20 turns for each conversation.

I have already trained over 1000 conversations yet the model does not seem to learn a bit. I wanted to know what common problems might be causing it to fail at all conversations. Am I training it wrong? Is my reward system incorrect? Or are 1000 conversations just not enough? Any help, tips that leads me in correct direction will be appreciated.


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