Pre-trained Word Embeddings for Goal-conditional Transfer Learning in Reinforcement Learning
This addresses sample efficiency for reinforcement learning agents in lifelong learning settings, but it is incremental as it applies existing methods to a new context.
The paper tackles the problem of low sample efficiency in reinforcement learning by using a pre-trained language model to enable goal-conditional transfer learning, resulting in improved performance on object navigation tasks.
Reinforcement learning (RL) algorithms typically start tabula rasa, without any prior knowledge of the environment, and without any prior skills. This however often leads to low sample efficiency, requiring a large amount of interaction with the environment. This is especially true in a lifelong learning setting, in which the agent needs to continually extend its capabilities. In this paper, we examine how a pre-trained task-independent language model can make a goal-conditional RL agent more sample efficient. We do this by facilitating transfer learning between different related tasks. We experimentally demonstrate our approach on a set of object navigation tasks.