Reduce memory usage in reinforcement learning #485
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…ules are evaluated
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david-hofmann
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Nov 11, 2024
* move `t_warmup` to kwargs * more RL save test * add `run_warmup` function * update `run_trial!` function * add getter functions for states and times when actions and learning rules are evaluated * update `run_experiment!` dispatches * move RL tests in a single file * remove `save_idxs` kwarg * import `CSV.write` for RL save
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Uses
saveatinrun_experiment!in order to reduce memory usage. For large systems of stiff equations my 16GB RAM maxes out even with moderate tspan (~100).Also reorganizes
run_experiment!dispatches to callrun_trial!internally.We could reduce memory even further by using
save_idxstoo, since we do not currently need all states saved. However this does not seem to work with time interpolation and indexing observed states, will investigate further in another PR.