GLIDE-RL: Grounded Language Instruction through DEmonstration in RL
Chaitanya Kharyal Sai Krishna Gottipati Tanmay Kumar Sinha Srijita Das Matthew E. Taylor
One of the final frontiers in the development of complex human - AI
collaborative systems is the ability of AI agents to comprehend the natural
language and perform tasks accordingly. However, training efficient
Reinforcement Learning (RL) agents grounded in natural language has been a
long-standing challenge due to the complexity and ambiguity of the language and
sparsity of the rewards, among other factors. Several advances in reinforcement
learning, curriculum learning, continual learning, language models have
independently contributed to effective training of grounded agents in various
environments. Leveraging these developments, we present a novel algorithm,
Grounded Language Instruction through DEmonstration in RL (GLIDE-RL) that
introduces a teacher-instructor-student curriculum learning framework for
training an RL agent capable of following natural language instructions that
can generalize to previously unseen language instructions. In this multi-agent
framework, the teacher and the student agents learn simultaneously based on the
student's current skill level. We further demonstrate the necessity for
training the student agent with not just one, but multiple teacher agents.
Experiments on a complex sparse reward environment validates the effectiveness
of our proposed approach.