OpenAi.Gym.Baselines.DeepQ Restoring checkpoint in next training

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OpenAi.Gym.Baselines.DeepQ Restoring checkpoint in next training

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Last edited Sep 2, 2017

Created on Sep 2, 2017

This example demonstrates a continuous training loop for OpenAI Gym’s CartPole-v0 using the Baselines DeepQ algorithm. It shows how a model checkpoint is saved and restored across alternating training and evaluation sessions, allowing training to resume seamlessly. The code uses `deepq.learn` and `deepq.load` from Baselines, with a custom `ActWrapper` that serializes the model via dill and TensorFlow’s `U.save_state`. A shell script alternates between `train_worker.py` and `enjoy_worker.py`, pausing training to render episodes and report rewards before resuming.

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Gist for continuous traing with Baselines DeepQ - https://github.com/openai/baselines

python continuous_trainer.py

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