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Found 12 papers, 2 papers with code
Controlled Text Generation with Adversarial Learning
In recent years, generative adversarial networks (GANs) have started to attain promising results also in natural language generation.
Truly No-Regret Learning in Constrained MDPs
As Efroni et al. (2020) pointed out, it is an open question whether primal-dual algorithms can provably achieve sublinear regret if we do not allow error cancellations.
Exploiting Causal Graph Priors with Posterior Sampling for Reinforcement Learning
The prior is typically specified as a class of parametric distributions, the design of which can be cumbersome in practice, often resulting in the choice of uninformative priors.
Provably Learning Nash Policies in Constrained Markov Potential Games
Multi-agent reinforcement learning (MARL) addresses sequential decision-making problems with multiple agents, where each agent optimizes its own objective.
Cancellation-Free Regret Bounds for Lagrangian Approaches in Constrained Markov Decision Processes
Unlike existing Lagrangian approaches, our algorithm achieves this regret without the need for the cancellation of errors.
Trust Region Policy Optimization with Optimal Transport Discrepancies: Duality and Algorithm for Continuous Actions
In this paper, we explore optimal transport discrepancies (which include the Wasserstein distance) to define trust regions, and we propose a novel algorithm - Optimal Transport Trust Region Policy Optimization (OT-TRPO) - for continuous state-action spaces.
Do you pay for Privacy in Online learning?
Online learning, in the mistake bound model, is one of the most fundamental concepts in learning theory.
Active Exploration for Inverse Reinforcement Learning
We propose a novel IRL algorithm: Active exploration for Inverse Reinforcement Learning (AceIRL), which actively explores an unknown environment and expert policy to quickly learn the expert's reward function and identify a good policy.
Learning in Non-Cooperative Configurable Markov Decision Processes
This presupposes that the two actors have the same reward functions.
Inverse Reinforcement Learning from a Gradient-based Learner
Inverse Reinforcement Learning addresses the problem of inferring an expert's reward function from demonstrations.
Newton Optimization on Helmholtz Decomposition for Continuous Games
In this paper, we propose NOHD (Newton Optimization on Helmholtz Decomposition), a Newton-like algorithm for multi-agent learning problems based on the decomposition of the dynamics of the system in its irrotational (Potential) and solenoidal (Hamiltonian) component.
T-CGAN: Conditional Generative Adversarial Network for Data Augmentation in Noisy Time Series with Irregular Sampling
Results show that classifiers trained on T-CGAN-generated data perform the same as classifiers trained on real data, even with very short time series and small training sets.
