Sarah_Riedmann:

I am trying to load a deep q model and retrain it with different parameters (e.g. changed epsilon value).


I’d have to experiment with this too. Since Checkpointer allows you to save and load not just the policy network state, but also the training state, I’d give that a go. In addition, it seems like it also caches into a replay buffer, which would be useful for sampling in DQN’s case. (tf_agents.utils.common.Checkpointer  |  TensorFlow Agents 4)
Hope this helps a little. cc @yablak @markdaoust