A Joint Imitation-Reinforcement Learning Framework for Reduced Baseline Regret

Official Repository for "A Joint Imitation-Reinforcement Learning (JIRL) Framework for Reduced Baseline Regret"

Technical Report

The report contains a detailed description of the experimental settings and hyperparameters used to obtain the results reported in our paper.

Objectives

1. Leveraging a baseline’s online demonstrations to minimize the regret w.r.t the baseline policy during training
2. Eventually surpassing the baseline performance

Assumptions

1. Access to a baseline policy at every time step
2. Uses an off-policy RL algorithm

Framework

Experiment Domains

Inverted pendulum

Lunar lander

Walker-2D

Lane following (CARLA)

Lane following (JetRacer)

Results

Performance

Inverted pendulum

Lunar lander

Lane following (CARLA)

Walker-2D

Lane following (JetRacer)

Baseline Regret

Lunar lander (JIRL vs TRPO)