We lifted 4 functions out of this paper's own repositories and ran 4 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 |
|---|---|---|
| copy not recorded | — | 4 of 4 |
| Function | Status | Where it lives |
|---|---|---|
| train | Ran | this paper's copy was not recorded; identical code first harvested from NathanGavenski/IUPE pointer only · get_code("65faa99ef0c486da") |
| train | Ran | this paper's copy was not recorded; identical code first harvested from NathanGavenski/IUPE pointer only · get_code("6cb5bfd9f6b92a48") |
| validation | Ran | this paper's copy was not recorded; identical code first harvested from NathanGavenski/IUPE pointer only · get_code("5c68d61a253a334d") |
| validation | Ran | this paper's copy was not recorded; identical code first harvested from NathanGavenski/IUPE pointer only · get_code("a90673e1d18b15a3") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Behavioral cloning is an imitation learning technique that teaches an agent how to behave through expert demonstrations. Recent approaches use self-supervision of fully-observable unlabeled snapshots of the states to decode state-pairs into actions. However, the iterative learning scheme from these techniques are prone to getting stuck into bad local minima. We address these limitations incorporating a two-phase model into the original framework, which learns from unlabeled observations via exploration, substantially improving traditional behavioral cloning by exploiting (i) a sampling mechanism to prevent bad local minima, (ii) a sampling mechanism to improve exploration, and (iii) self-attention modules to capture global features. The resulting technique outperforms the previous state-of-the-art in four different environments by a large margin.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2008.05660")
get_code_for_paper("2008.05660")
have("2008.05660")
Connect an agent — have() is free.