Mix & match agent curricula for reinforcement learning
W Czarnecki, S Jayakumar… - International …, 2018 - proceedings.mlr.press
International Conference on Machine Learning, 2018•proceedings.mlr.press
We introduce Mix and match (M&M)–a training framework designed to facilitate rapid and
effective learning in RL agents that would be too slow or too challenging to train otherwise.
The key innovation is a procedure that allows us to automatically form a curriculum over
agents. Through such a curriculum we can progressively train more complex agents by,
effectively, bootstrapping from solutions found by simpler agents. In contradistinction to
typical curriculum learning approaches, we do not gradually modify the tasks or …
effective learning in RL agents that would be too slow or too challenging to train otherwise.
The key innovation is a procedure that allows us to automatically form a curriculum over
agents. Through such a curriculum we can progressively train more complex agents by,
effectively, bootstrapping from solutions found by simpler agents. In contradistinction to
typical curriculum learning approaches, we do not gradually modify the tasks or …
Abstract
We introduce Mix and match (M&M)–a training framework designed to facilitate rapid and effective learning in RL agents that would be too slow or too challenging to train otherwise. The key innovation is a procedure that allows us to automatically form a curriculum over agents. Through such a curriculum we can progressively train more complex agents by, effectively, bootstrapping from solutions found by simpler agents. In contradistinction to typical curriculum learning approaches, we do not gradually modify the tasks or environments presented, but instead use a process to gradually alter how the policy is represented internally. We show the broad applicability of our method by demonstrating significant performance gains in three different experimental setups:(1) We train an agent able to control more than 700 actions in a challenging 3D first-person task; using our method to progress through an action-space curriculum we achieve both faster training and better final performance than one obtains using traditional methods.(2) We further show that M&M can be used successfully to progress through a curriculum of architectural variants defining an agents internal state.(3) Finally, we illustrate how a variant of our method can be used to improve agent performance in a multitask setting.
proceedings.mlr.press