In this paper, weunifiedthe ranking process under general stochastic click models as a Markov Decision Process (MDP), and the optimal ranking could be learned with offlinereinforcementlearning (RL) directly. Building upon this, we leverage offline RL techniques foroff-policyLTR...
In this paper, weunifiedthe ranking process under general stochastic click models as a Markov Decision Process (MDP), and the optimal ranking could be learned with offlinereinforcementlearning (RL) directly. Building upon this, we leverage offline RL techniques foroff-policyLTR...
In this paper, weunifiedthe ranking process under general stochastic click models as a Markov Decision Process (MDP), and the optimal ranking could be learned with offlinereinforcementlearning (RL) directly. Building upon this, we leverage offline RL techniques foroff-policyLTR...
Furthermore, visual representations like the one above help us fully grasp the concept of Unified Off-Policy Reinforcement.
Policyevaluation inreinforcementlearning is often conducted using two-timescale stochastic approximation, which results in various gradient temporal difference methods such as GTD(0), GTD2, and TDC. Here, we provide convergence rate bounds for this suite of algorithms.
Unifiedoff-policyRL perspectives enable direct application to problems such as learning to rank under general user click models via MDP reformulation and offline RL algorithms, eliminating the need for explicit de-biasing based on click model assumptions (Zhang et al., 2023).
ReVal is a noveloff-policyvalue-basedreinforcementlearning framework for LLM post-training that interprets model logits as Q-values to enable efficient experience replay.
