Comments (1)
ruiwang2uber
commented on July 17, 2024
1
Hello @RihabGor ,
Thanks for your interest in VINE.
es_modified.py is an example where you need to save the data for VINE.
https://github.com/uber-common/deep-neuroevolution/blob/master/es_distributed/es_modified.py#L179
The basic format is: each row for one of the psuedo-offspings
[RAM, fitness, length, noise_idx, policy_seed, sign]
To prepare data for VINE:
When training, please replace es.py with es_modified.py
Then automatically all the data (final RAM state) is saved for you and can directly consumed by VINE.
from deep-neuroevolution.
Related Issues (20)
- proposed fix for nan error in vine HOT 1
- how to run locally
- AttributeError: 'list' object has no attribute 'ndim' HOT 1
- Elitism
- how to run gpu_implementation on GPU HOT 3
- [Local ES Frostbite] Value Error, Dimension Mismatch HOT 1
- "No ROM File specified or the ROM file was not found" HOT 1
- [es_distributed/ga.py] eval_rews, eval_length = policy.rollout(env) ValueError: too many values to unpack (expected 2)
- [es_distributed/rs.py] eval_rews, eval_length = policy.rollout(env) ValueError: too many values to unpack (expected 2)
- [es_distributed/tf_util.py] ValueError: Dimension 0 in both shapes must be equal, but are 4608 and 18. Shapes are [4608] and [18]. From merging shape 12 with other shapes. for 'concat/concat_dim' (op: 'Pack') with input shapes: [4096], [16], [16], [16], [8192], [32], [32], [32], [991232], [ 256], [256], [256], [4608], [18]. HOT 1
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from deep-neuroevolution.