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Found 6 papers, 4 papers with code
Mimicking Better by Matching the Approximate Action Distribution
In this paper, we introduce MAAD, a novel, sample-efficient on-policy algorithm for Imitation Learning from Observations.
Optimality Inductive Biases and Agnostic Guidelines for Offline Reinforcement Learning
Only our framework allowed us to design a method that performed well across the spectrum while remaining modular if more information about the quality of the data ever becomes available.
Conditional Neural Relational Inference for Interacting Systems
In this work, we want to learn to model the dynamics of similar yet distinct groups of interacting objects.
Lipschitzness Is All You Need To Tame Off-policy Generative Adversarial Imitation Learning
Despite the recent success of reinforcement learning in various domains, these approaches remain, for the most part, deterringly sensitive to hyper-parameters and are often riddled with essential engineering feats allowing their success.
Relational Mimic for Visual Adversarial Imitation Learning
In this work, we introduce a new method for imitation learning from video demonstrations.
Sample-Efficient Imitation Learning via Generative Adversarial Nets
GAIL is a recent successful imitation learning architecture that exploits the adversarial training procedure introduced in GANs.
