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Found 8 papers, 4 papers with code
What can online reinforcement learning with function approximation benefit from general coverage conditions?
In online reinforcement learning (RL), instead of employing standard structural assumptions on Markov decision processes (MDPs), using a certain coverage condition (original from offline RL) is enough to ensure sample-efficient guarantees (Xie et al. 2023).
Identifiability and generalizability from multiple experts in Inverse Reinforcement Learning
While Reinforcement Learning (RL) aims to train an agent from a reward function in a given environment, Inverse Reinforcement Learning (IRL) seeks to recover the reward function from observing an expert's behavior.
Proximal Point Imitation Learning
Thanks to PPM, we avoid nested policy evaluation and cost updates for online IL appearing in the prior literature.
Understanding Deep Neural Function Approximation in Reinforcement Learning via $ε$-Greedy Exploration
To be specific, we focus on the value based algorithm with the $\epsilon$-greedy exploration via deep (and two-layer) neural networks endowed by Besov (and Barron) function spaces, respectively, which aims at approximating an $\alpha$-smooth Q-function in a $d$-dimensional feature space.
Neural NID Rules
Abstract object properties and their relations are deeply rooted in human common sense, allowing people to predict the dynamics of the world even in situations that are novel but governed by familiar laws of physics.
Common Sense Reasoning
Model-based Reinforcement Learning
+3
Robust Learning from Observation with Model Misspecification
Imitation learning (IL) is a popular paradigm for training policies in robotic systems when specifying the reward function is difficult.
Sample-efficient actor-critic algorithms with an etiquette for zero-sum Markov games
Our sample complexities also match the best-known results for global convergence of policy gradient and two time-scale actor-critic algorithms in the single agent setting.
Robust Inverse Reinforcement Learning under Transition Dynamics Mismatch
We study the inverse reinforcement learning (IRL) problem under a transition dynamics mismatch between the expert and the learner.
