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Found 5 papers, 3 papers with code
A Black-box Approach for Non-stationary Multi-agent Reinforcement Learning
Specifically, we focus on games with bandit feedback, where testing an equilibrium can result in substantial regret even when the gap to be tested is small, and the existence of multiple optimal solutions (equilibria) in stationary games poses extra challenges.
Offline Meta Reinforcement Learning with In-Distribution Online Adaptation
We find a return-based uncertainty quantification for IDAQ that performs effectively.
Practically Solving LPN in High Noise Regimes Faster Using Neural Networks
For some settings we are also able to provide theories that explain the rationale of the design of our models.
Offline congestion games: How feedback type affects data coverage requirement
Starting from the facility-level (a. k. a., semi-bandit) feedback, we propose a novel one-unit deviation coverage condition and give a pessimism-type algorithm that can recover an approximate NE.
Offline Reinforcement Learning with Reverse Model-based Imagination
These reverse imaginations provide informed data augmentation for model-free policy learning and enable conservative generalization beyond the offline dataset.
