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the answer is adding lambda inputs: inputs["your_key_for_observation"] to the network in case someone encounters this issue in the future


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This is the original Q-Learning paper by Watkins, though you may need to pay for access to this. This is the Nature paper that introduced the DQN.


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The value of the objective depends on policy (probabilities of taking an action). Intuitively speaking, better actions lead to better returns and by "pushing up" the probabilities (log or not same thing since log is monotonically increasing function) of those actions you're making sure you're getting better returns and increasing the value of your ...


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Episodes are discrete, there is no need for calculus. Your "sample efficiency" metric is: $$\sum_{x=a}^b R_x$$ The quantity you are measuring per episode is the return (undiscounted). The sum of this over many episodes does not measure sample efficiency as the term is usually meant, although the sample efficiency of the algorithm you use should ...


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How does the neural network learn to differentiate between good and bad actions? Good actions - in context of a given state - have higher return than bad actions on average, taken over many examples where the actions occur in different combinations. In REINFORCE, when training the neural network, all actions are effectively treated as ground truth "...


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