Semi-supervised Offline Reinforcement Learning with Pre-trained Decision Transformers

no code implementations • 29 Sep 2021 • Catherine Cang, Kourosh Hakhamaneshi, Ryan Rudes, Igor Mordatch, Aravind Rajeswaran, Pieter Abbeel, Michael Laskin

In this paper, we investigate how we can leverage large reward-free (i. e. task-agnostic) offline datasets of prior interactions to pre-train agents that can then be fine-tuned using a small reward-annotated dataset.

D4RL Offline RL +2

Data-Efficient Exploration with Self Play for Atari

no code implementations • ICML Workshop URL 2021 • Michael Laskin, Catherine Cang, Ryan Rudes, Pieter Abbeel

To alleviate the reliance on reward engineering it is important to develop RL algorithms capable of efficiently acquiring skills with no rewards extrinsic to the agent.

Efficient Exploration Reinforcement Learning (RL)