Comments (1)
That's a valid point though I think it's valid and fair game to assume the agent can continue to learn and adjust it's policy during evaluation as long as its state (Q-function) is reset at the beginning of each episode to what it was after the learning phase (which might not be currently the case and may need to be fixed.)
Also if we really want to handicap the agent and prevent it to learn during an episode then I suggest we add a freeze_learning:bool
parameter to DeepQLearnerPolicy
agent. If set to true
then function update_q_function
becomes a no-op.
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