Comments (1)
Not much improvement is observed after adding GPU support. This is mainly because the majority of computation happens outside GPU and are not available for parallelization. If it is possible to put multiple agents into a parallelizable code, then GPU support can be added to this.
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Related Issues (14)
- Continuous Integration
- Write tests for random walk
- Inconsistency between branches HOT 6
- Use of GAE
- Performance issues: When training is off, the model takes much longer to give samples (incorrect use of volatile?)
- Use Learned Value function HOT 4
- Evaluate posterior at the end of training
- cProfiling HOT 4
- Update to PyTorch 0.4
- Optimize Chi-squared objective rather than KL objective
- Make ISSampler and MCSampler work in batch mode for more efficient sample collection
- Scale walk to higher dimensions.
- Add checkpointing
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