OpenAI Blog
Equivalence between policy gradients and soft Q-learning
Read the original on OpenAI Blog →The Flow has not summarised this story yet — read it at OpenAI Blog.
Related stories
arXiv Machine Learning
Aug 11
Regret of exploratory policy improvement and $q$-learning
arXiv:2411. 01302v2 Announce Type: replace Abstract: We study the convergence of $q$-learning and related algorithms introduced by Jia and Zhou (J.
arXiv AI
Jul 20
Soft $Q(\lambda)$: A multi-step off-policy method for entropy regularised reinforcement learning using eligibility traces
arXiv:2604. 13780v2 Announce Type: replace-cross Abstract: Soft Q-learning has emerged as a versatile model-free method for entropy-regularised reinforcement learning, optimising for returns augmented with a penalty on the divergence from a reference policy.
arXiv AI
Jun 16
Deep Q-Learning on H\"older Spaces
arXiv:2606. 16846v1 Announce Type: cross Abstract: We study the operator-theoretic core of Q-learning in continuous-time stochastic control with continuous states and actions.
arXiv AI
Jun 19
Stabilizing the Q-Gradient Field for Policy Smoothness in Actor-Critic Methods
arXiv:2601. 22970v2 Announce Type: replace-cross Abstract: Policies learned via continuous actor-critic methods often exhibit erratic, high-frequency oscillations, making them unsuitable for physical deployment.
arXiv AI
Jun 18
Pareto Q-Learning with Reward Machines
arXiv:2606. 19134v1 Announce Type: cross Abstract: We present Pareto Q-Learning with Reward Machines (PQLRM), a multi-objective reinforcement learning algorithm for tasks whose reward structure is specified by a set of reward machines (RMs).
arXiv AI
Jul 10
Spectral Analysis of Dueling Q-Learning
arXiv:2607. 08340v1 Announce Type: cross Abstract: Q-learning is a fundamental algorithm in reinforcement learning (RL) for solving discounted Markov decision processes (MDPs) when the transition kernel is unknown.