Nan Jiang, Siyuan Zhang
We lifted 2 functions out of this paper's own repositories and ran 0 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| jasonzhang929/BVFT_empirical_experiments | canonical | 0 of 2 |
| Function | Status | Where it lives |
|---|---|---|
| get_data | Not yet run | jasonzhang929/BVFT_empirical_experiments/run_exp.py pointer only (licence: NONE) · get_code("8584f8e013f397e1") |
| get_file_names | Not yet run | jasonzhang929/BVFT_empirical_experiments/run_exp.py pointer only (licence: NONE) · get_code("dc49e9f62cb45a02") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
How to select between policies and value functions produced by different training algorithms in offline reinforcement learning (RL)-which is crucial for hyperparameter tuning-is an important open question. Existing approaches based on off-policy evaluation (OPE) often require additional function approximation and hence hyperparameters, creating a chicken-and-egg situation. In this paper, we design hyperparameter-free algorithms for policy selection based on BVFT [XJ21], a recent theoretical advance in value-function selection, and demonstrate their effectiveness in discrete-action benchmarks such as Atari. To address performance degradation due to poor critics in continuous-action domains, we further combine BVFT with OPE to get the best of both worlds, and obtain a hyperparameter-tuning method for Q-function based OPE with theoretical guarantees as a side product. 35th Conference on Neural Information Processing Systems (NeurIPS 2021).
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2110.14000")
get_code_for_paper("2110.14000")
have("2110.14000")
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