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Found 4 papers, 1 papers with code
State-Action Similarity-Based Representations for Off-Policy Evaluation
Instead, in this paper, we seek to enhance the data-efficiency of FQE by first transforming the fixed dataset using a learned encoder, and then feeding the transformed dataset into FQE.
Learning to Stabilize Online Reinforcement Learning in Unbounded State Spaces
This latter objective is called stability and is especially important when the state space is unbounded, such that the states can be arbitrarily far from each other and the agent can drift far away from the desired states.
Scaling Marginalized Importance Sampling to High-Dimensional State-Spaces via State Abstraction
We consider the problem of off-policy evaluation (OPE) in reinforcement learning (RL), where the goal is to estimate the performance of an evaluation policy, $\pi_e$, using a fixed dataset, $\mathcal{D}$, collected by one or more policies that may be different from $\pi_e$.
RIDM: Reinforced Inverse Dynamics Modeling for Learning from a Single Observed Demonstration
Augmenting reinforcement learning with imitation learning is often hailed as a method by which to improve upon learning from scratch.
