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Look into the number of optimisers one needs and how they interact
Next Steps
- Evaluation to calc gain (return) every n steps (freeze workers?) just copy the global network, run some eval rollouts and plot
- Tensorboard:
- critic loss, actor loss, network stats? grads?
- create additional worker class to monitor for TFBoard
Extract code to .py
- setup new conda env with box2D
- New pycharm project with conda env
Add a parser
code version of A3C with shared network
Implement full loss?
total loss=critic coefficient∗critic loss+actor coefficient∗actor loss+entropy coefficient∗entropy regularisation
Usually critic coefficient = 0.5, actor coefficient = 1.0 and entropy coefficient, called β in the paper = 0.01.
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