31 citations · 67 across the 8 of their papers we have counts for
8 papers
Know Your Boundaries: The Necessity of Explicit Behavioral Cloning in Offline RL
Wonjoon Goo, Scott Niekum
We introduce an offline reinforcement learning (RL) algorithm that explicitly clones a behavior policy to constrain value learning. In offline RL, it is often important to prevent…
A Ranking Game for Imitation Learning
Harshit Sikchi, Akanksha Saran, Wonjoon Goo +1
We propose a new framework for imitation learning -- treating imitation as a two-player ranking-based game between a policy and a reward. In this game, the reward agent learns to s…
You Only Evaluate Once: a Simple Baseline Algorithm for Offline RL
Wonjoon Goo, Scott Niekum
The goal of offline reinforcement learning (RL) is to find an optimal policy given prerecorded trajectories. Many current approaches customize existing off-policy RL algorithms, es…
Self-Supervised Online Reward Shaping in Sparse-Reward Environments
Farzan Memarian, Wonjoon Goo, Rudolf Lioutikov +2
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…
Local Nonparametric Meta-Learning
Wonjoon Goo, Scott Niekum
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. Most meta-le…
Better-than-Demonstrator Imitation Learning via Automatically-Ranked Demonstrations
Daniel S. Brown, Wonjoon Goo, Scott Niekum
The performance of imitation learning is typically upper-bounded by the performance of the demonstrator. While recent empirical results demonstrate that ranked demonstrations allow…