We lifted 3 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 |
|---|---|---|
| copy not recorded | — | 3 of 3 |
| Function | Status | Where it lives |
|---|---|---|
| get_v | Ran | this paper's copy was not recorded; identical code first harvested from archit120/Risk-Aware-Active-IRL pointer only · get_code("ab0d051f811f37f3") |
| get_v_expert | Ran | this paper's copy was not recorded; identical code first harvested from archit120/Risk-Aware-Active-IRL pointer only · get_code("50fef2dd852d7e5a") |
| sample_rewards | Ran | this paper's copy was not recorded; identical code first harvested from archit120/Risk-Aware-Active-IRL pointer only · get_code("7d7418b1e6c542e3") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Active learning from demonstration allows a robot to query a human for specific types of input to achieve efficient learning. Existing work has explored a variety of active query strategies; however, to our knowledge, none of these strategies directly minimize the performance risk of the policy the robot is learning. Utilizing recent advances in performance bounds for inverse reinforcement learning, we propose a risk-aware active inverse reinforcement learning algorithm that focuses active queries on areas of the state space with the potential for large generalization error. We show that risk-aware active learning outperforms standard active IRL approaches on gridworld, simulated driving, and table setting tasks, while also providing a performance-based stopping criterion that allows a robot to know when it has received enough demonstrations to safely perform a task.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("1901.02161")
get_code_for_paper("1901.02161")
have("1901.02161")
Connect an agent — have() is free.