How a simple choice shapes exploration, safety, and efficiency The post The Fundamental Choice in Reinforcement Learning: On‑Policy vs. Off‑Policy appeared first on Towards Data Science .
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arXiv:2510. 03494v2 Announce Type: replace Abstract: We study finite-horizon offline reinforcement learning (RL) with function approximation for both policy evaluation and policy optimization.
arXiv:2406.03894v2 Announce Type: replace Abstract: Proximal Policy Optimization (PPO) is a popular model-free reinforcement learning algorithm, esteemed for its simplicity and efficacy. However, due...
arXiv:2406.03678v2 Announce Type: replace Abstract: On-policy reinforcement learning methods, like Trust Region Policy Optimization (TRPO) and Proximal Policy Optimization (PPO), often demand extensi...
arXiv:2601. 19612v3 Announce Type: replace-cross Abstract: Safe exploration is a key requirement for reinforcement learning (RL) agents to learn and adapt online, beyond controlled (e.
The paper offers a new way to view the occupancy measure in reinforcement learning by embedding the planning criterion into the dynamics via a resetting planning process. The resulting stationary measure, called the visitation measure, forms a dually flat statistical manifold with two affine charts: visitation probabilities and log-policies, which are dual under conditional entropy. This geometric framework allows planning-as-inference to extend beyond linear rewards to nonlinear functionals of visitation, with each iteration solvable by a natural-gradient step and provides a new interpretation of the temporal-difference error as a marginal-utility estimate.