Search Results for author:
Found 8 papers, 5 papers with code
Know Your Boundaries: The Necessity of Explicit Behavioral Cloning in Offline RL
In this work, we argue that it is not only viable but beneficial to explicitly model the behavior policy for offline RL because the constraint can be realized in a stable way with the trained model.
A Ranking Game for Imitation Learning
We propose a new framework for imitation learning -- treating imitation as a two-player ranking-based game between a policy and a reward.
You Only Evaluate Once: a Simple Baseline Algorithm for Offline RL
The goal of offline reinforcement learning (RL) is to find an optimal policy given prerecorded trajectories.
Self-Supervised Online Reward Shaping in Sparse-Reward Environments
We introduce Self-supervised Online Reward Shaping (SORS), which aims to improve the sample efficiency of any RL algorithm in sparse-reward environments by automatically densifying rewards.
Local Nonparametric Meta-Learning
A central goal of meta-learning is to find a learning rule that enables fast adaptation across a set of tasks, by learning the appropriate inductive bias for that set.
Better-than-Demonstrator Imitation Learning via Automatically-Ranked Demonstrations
The performance of imitation learning is typically upper-bounded by the performance of the demonstrator.
Extrapolating Beyond Suboptimal Demonstrations via Inverse Reinforcement Learning from Observations
A critical flaw of existing inverse reinforcement learning (IRL) methods is their inability to significantly outperform the demonstrator.
One-Shot Learning of Multi-Step Tasks from Observation via Activity Localization in Auxiliary Video
Due to burdensome data requirements, learning from demonstration often falls short of its promise to allow users to quickly and naturally program robots.
