Multi-Goal Reinforcement Learning: Challenging Robotics Environments and Request for Research
This work provides new benchmarks for robotics and RL research, but it is incremental as it builds on existing frameworks and ideas.
The paper introduces a suite of challenging robotics tasks based on existing hardware, integrated with OpenAI Gym, and presents research ideas for improving RL algorithms, particularly in Multi-Goal RL and Hindsight Experience Replay.
The purpose of this technical report is two-fold. First of all, it introduces a suite of challenging continuous control tasks (integrated with OpenAI Gym) based on currently existing robotics hardware. The tasks include pushing, sliding and pick & place with a Fetch robotic arm as well as in-hand object manipulation with a Shadow Dexterous Hand. All tasks have sparse binary rewards and follow a Multi-Goal Reinforcement Learning (RL) framework in which an agent is told what to do using an additional input. The second part of the paper presents a set of concrete research ideas for improving RL algorithms, most of which are related to Multi-Goal RL and Hindsight Experience Replay.