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Found 4 papers, 2 papers with code
Reevaluating Policy Gradient Methods for Imperfect-Information Games
In the past decade, motivated by the putative failure of naive self-play deep reinforcement learning (DRL) in adversarial imperfect-information games, researchers have developed numerous DRL algorithms based on fictitious play (FP), double oracle (DO), and counterfactual regret minimization (CFR).
Decoupling regularization from the action space
While standard unregularized RL methods remain unaffected by changes in the number of actions, we show that it can severely impact their regularized counterparts.
Maximum entropy GFlowNets with soft Q-learning
Generative Flow Networks (GFNs) have emerged as a powerful tool for sampling discrete objects from unnormalized distributions, offering a scalable alternative to Markov Chain Monte Carlo (MCMC) methods.
Arc travel time and path choice model estimation subsumed
We propose a method for maximum likelihood estimation of path choice model parameters and arc travel time using data of different levels of granularity.
