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Found 4 papers, 0 papers with code
Regularizing Adversarial Imitation Learning Using Causal Invariance
Imitation learning methods are used to infer a policy in a Markov decision process from a dataset of expert demonstrations by minimizing a divergence measure between the empirical state occupancy measures of the expert and the policy.
Learning Invariant Reward Functions through Trajectory Interventions
Inverse reinforcement learning methods aim to retrieve the reward function of a Markov decision process based on a dataset of expert demonstrations.
Inverse Reinforcement Learning via Matching of Optimality Profiles
Given an optimality profile and a small amount of additional supervision, our algorithm fits a reward function, modeled as a neural network, by essentially minimizing the Wasserstein distance between the corresponding induced distribution and the optimality profile.
Poincaré Wasserstein Autoencoder
This work presents a reformulation of the recently proposed Wasserstein autoencoder framework on a non-Euclidean manifold, the Poincar\'e ball model of the hyperbolic space.
