Search Results for author:
Found 2 papers, 0 papers with code
Imitation Learning by State-Only Distribution Matching
We propose a non-adversarial learning-from-observations approach, together with an interpretable convergence and performance metric.
State-Only Imitation Learning by Trajectory Distribution Matching
Our training objective minimizes the Kulback-Leibler divergence between the policy and expert state transition trajectories which can be optimized in a non-adversarial fashion.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
