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Found 5 papers, 3 papers with code
Importance Weighted Actor-Critic for Optimal Conservative Offline Reinforcement Learning
We propose A-Crab (Actor-Critic Regularized by Average Bellman error), a new practical algorithm for offline reinforcement learning (RL) in complex environments with insufficient data coverage.
Optimal Conservative Offline RL with General Function Approximation via Augmented Lagrangian
Offline reinforcement learning (RL), which refers to decision-making from a previously-collected dataset of interactions, has received significant attention over the past years.
MADE: Exploration via Maximizing Deviation from Explored Regions
As a proof of concept, we evaluate the new intrinsic reward on tabular examples across a variety of model-based and model-free algorithms, showing improvements over count-only exploration strategies.
Bridging Offline Reinforcement Learning and Imitation Learning: A Tale of Pessimism
Based on the composition of the offline dataset, two main categories of methods are used: imitation learning which is suitable for expert datasets and vanilla offline RL which often requires uniform coverage datasets.
SLIP: Learning to Predict in Unknown Dynamical Systems with Long-Term Memory
Our theoretical and experimental results shed light on the conditions required for efficient probably approximately correct (PAC) learning of the Kalman filter from partially observed data.
