We lifted 6 functions out of this paper's own repositories and ran 5 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 |
|---|---|---|
| lanyavik/BAIL | canonical | 5 of 6 |
| Function | Status | Where it lives |
|---|---|---|
| L2PenaltyLoss | Ran | lanyavik/BAIL/spinup/spinup/algos/BAIL/bail_training.py pointer only (licence: NONE) · get_code("303c000a48de41a3") |
| calc_ue_valiloss | Ran | lanyavik/BAIL/spinup/spinup/algos/BAIL/bail_training.py pointer only (licence: NONE) · get_code("475ab1256cf40ee1") |
| calculate_mc_gain | Ran | lanyavik/BAIL/spinup/spinup/algos/BAIL/main_get_mcret.py pointer only (licence: NONE) · get_code("a8ecbc5e2b2fefaf") |
| calculate_mc_return_no_aug | Ran | lanyavik/BAIL/spinup/spinup/algos/BAIL/main_get_mcret.py pointer only (licence: NONE) · get_code("70d424a5dbf32a39") |
| evaluate_policy | Ran | lanyavik/BAIL/spinup/spinup/algos/BAIL/main_static_bail.py pointer only (licence: NONE) · get_code("c74671448905c5d4") |
| train_upper_envelope | Not yet run | lanyavik/BAIL/spinup/spinup/algos/BAIL/bail_training.py pointer only (licence: NONE) · get_code("72d5b12cffbf9925") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
There has recently been a surge in research in batch Deep Reinforcement Learning (DRL), which aims for learning a high-performing policy from a given dataset without additional interactions with the environment. We propose a new algorithm, Best-Action Imitation Learning (BAIL), which strives for both simplicity and performance. BAIL learns a V function, uses the V function to select actions it believes to be high-performing, and then uses those actions to train a policy network using imitation learning. For the MuJoCo benchmark, we provide a comprehensive experimental study of BAIL, comparing its performance to four other batch Q-learning and imitation-learning schemes for a large variety of batch datasets. Our experiments show that BAIL's performance is much higher than the other schemes, and is also computationally much faster than the batch Q-learning schemes.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("1910.12179")
get_code_for_paper("1910.12179")
have("1910.12179")
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