We lifted 1 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 |
|---|---|---|
| hmhyau/rl-intention | canonical | 0 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| compute_q_from_intention | Not yet run | hmhyau/rl-intention/train_blackjack.py code served (permissive licence) · get_code("918a022c2be53298") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
We present a novel form of explanation for Reinforcement Learning, based around the notion of intended outcome. These explanations describe the outcome an agent is trying to achieve by its actions. We provide a simple proof that general methods for post-hoc explanations of this nature are impossible in traditional reinforcement learning. Rather, the information needed for the explanations must be collected in conjunction with training the agent. We derive approaches designed to extract local explanations based on intention for several variants of Q-function approximation and prove consistency between the explanations and the Q-values learned. We demonstrate our method on multiple reinforcement learning problems, and provide code to help researchers introspecting their RL environments and algorithms.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2011.05064")
get_code_for_paper("2011.05064")
have("2011.05064")
Connect an agent — have() is free.