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TAXONS
0.1
Task Agnostic eXploration of Outcome spaces through Novelty and Surprise
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Functions | |
| def | main (seed, params) |
Variables | |
| enabled | |
| p = parameters.Params() | |
| parallel_threads = p.threads | |
| list | seeds |
| list | multiseeds = [] |
| int | total_train_time = 0 |
| list | params = [parameters.Params() for i in range(len(seeds))] |
| nodes = min(len(seeds), pathos.threading.cpu_count()-1) | |
| start_time = time.monotonic() | |
| results = pool.map(main, seeds, params) | |
| end_time = time.monotonic() | |
| bool | end = False |
| def train.main | ( | seed, | |
| params | |||
| ) |
| train.enabled |
| bool train.end = False |
| train.end_time = time.monotonic() |
| list train.multiseeds = [] |
| train.nodes = min(len(seeds), pathos.threading.cpu_count()-1) |
| train.p = parameters.Params() |
| train.parallel_threads = p.threads |
| list train.params = [parameters.Params() for i in range(len(seeds))] |
| list train.seeds |
| train.start_time = time.monotonic() |
| int train.total_train_time = 0 |