We lifted 1 functions out of this paper's own repositories and ran 1 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 |
|---|---|---|
| sjunhongshen/Deterministic-Control-in-Metric-Space | reimplementation | 1 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| crop_center | Ran | sjunhongshen/Deterministic-Control-in-Metric-Space/cartpole_images.py pointer only (licence: NONE) · get_code("fd18c8c90c72a792") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
We study online reinforcement learning for finite-horizon deterministic control systems with {\it arbitrary} state and action spaces. Suppose that the transition dynamics and reward function is unknown, but the state and action space is endowed with a metric that characterizes the proximity between different states and actions. We provide a surprisingly simple upper-confidence reinforcement learning algorithm that uses a function approximation oracle to estimate optimistic Q functions from experiences. We show that the regret of the algorithm after $K$ episodes is $O(HL(KH)^{\frac{d-1}{d}}) $ where $L$ is a smoothness parameter, and $d$ is the doubling dimension of the state-action space with respect to the given metric. We also establish a near-matching regret lower bound. The proposed method can be adapted to work for more structured transition systems, including the finite-state case and the case where value functions are linear combinations of features, where the method also achieve the optimal regret.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("1905.01576")
get_code_for_paper("1905.01576")
have("1905.01576")
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