We lifted 5 functions out of this paper's own repositories and ran 3 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 |
|---|---|---|
| jakegrigsby/cc-afbc | canonical | 3 of 5 |
| Function | Status | Where it lives |
|---|---|---|
| get_linear_schedule | Ran | jakegrigsby/cc-afbc/filters.py code served (permissive licence) · get_code("63bb6952481500e6") |
| robosuite_action_adjustment | Ran | jakegrigsby/cc-afbc/rl_utils/envs.py code served (permissive licence) · get_code("ab198159e66084fd") |
| unique | Ran | jakegrigsby/cc-afbc/rl_utils/replay.py code served (permissive licence) · get_code("cf11c744c61b2185") |
| highway_env | Not yet run | jakegrigsby/cc-afbc/rl_utils/envs.py code served (permissive licence) · get_code("771d49bb23e04b6c") |
| load_gym | Not yet run | jakegrigsby/cc-afbc/rl_utils/envs.py code served (permissive licence) · get_code("d13ad67924e983b5") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Recent Offline Reinforcement Learning methods have succeeded in learning high-performance policies from fixed datasets of experience. A particularly effective approach learns to first identify and then mimic optimal decision-making strategies. Our work evaluates this method's ability to scale to vast datasets consisting almost entirely of sub-optimal noise. A thorough investigation on a custom benchmark helps identify several key challenges involved in learning from high-noise datasets. We re-purpose prioritized experience sampling to locate expert-level demonstrations among millions of low-performance samples. This modification enables offline agents to learn state-of-the-art policies in benchmark tasks using datasets where expert actions are outnumbered nearly 65:1.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2110.04698")
get_code_for_paper("2110.04698")
have("2110.04698")
Connect an agent — have() is free.