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I am working on training a reinforcement learning agent with a huge dataset of past human players' experience. Each user had to independently increase their game score. I am using the dataset because I have plenty of data and because on-policy training can be slow due to the game speed.

The only problem I have is the human player ID as a categorical feature. I have around 1000 experience play from each user and an ID should be assigned so the LSTMs can detect the sequential behavior of the game play.

Should I:

1- Serve each player's experience as an episode to the agent so to get rid of user IDs as a category

2- or keep the training batches together including all the user IDs and train the agenet with them?

The problem here is that it is complicated for me to use one-hot encode for this large category.

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If you're not explicitly modelling the player's behavior, then using IDs doesn't make any sense. The point of using categorical features is to use them in prediction - the player IDs aren't part of environment, right?

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  • $\begingroup$ The player IDs were proposed so the agent during the pre-training understands which user started which game i.e. the training batch contains several users with their current state/action at a certain date. Now, if we serve the training batches player by player then there's no need for their IDs. $\endgroup$ – Leb_Broth Apr 7 '18 at 15:05
  • $\begingroup$ How are you going to use these IDs for unseen data? $\endgroup$ – Jakub Bartczuk Apr 7 '18 at 15:35
  • $\begingroup$ The data is the state/action for each player in the past. Assigning to them their IDs in this case, would let the LSTM get the temporal dependency for each player. Am i missing something? $\endgroup$ – Leb_Broth Apr 7 '18 at 15:39
  • $\begingroup$ What I mean is why do you even use this data? It seems to me that RL agent is supposed to react to environment, and I don't think what player ID has to do with that $\endgroup$ – Jakub Bartczuk Apr 7 '18 at 15:42
  • $\begingroup$ Yes exactly. But my environment is very slow for the agent to learn from it. That is why i decided to pre-train it by demonstration. Is there an alternative for the training in case of slow environment? Should I create my own gym maybe in an accelerated way? $\endgroup$ – Leb_Broth Apr 7 '18 at 15:44

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