Language as a Cognitive Tool to Imagine Goals in Curiosity-Driven Exploration

by   Cédric Colas, et al.

Autonomous reinforcement learning agents must be intrinsically motivated to explore their environment, discover potential goals, represent them and learn how to achieve them. As children do the same, they benefit from exposure to language, using it to formulate goals and imagine new ones as they learn their meaning. In our proposed learning architecture (IMAGINE), the agent freely explores its environment and turns natural language descriptions of interesting interactions from a social partner into potential goals. IMAGINE learns to represent goals by jointly learning a language model and a goal-conditioned reward function. Just like humans, our agent uses language compositionality to generate new goals by composing known ones. Leveraging modular model architectures based on Deep Sets and gated-attention mechanisms, IMAGINE autonomously builds a repertoire of behaviors and shows good zero-shot generalization properties for various types of generalization. When imagining its own goals, the agent leverages zero-shot generalization of the reward function to further train on imagined goals and refine its behavior. We present experiments in a simulated domain where the agent interacts with procedurally generated scenes containing objects of various types and colors, discovers goals, imagines others and learns to achieve them.


page 5

page 9

page 10

page 12

page 13

page 14

page 15

page 19


Language Grounding through Social Interactions and Curiosity-Driven Multi-Goal Learning

Autonomous reinforcement learning agents, like children, do not have acc...

Deep Sets for Generalization in RL

This paper investigates the idea of encoding object-centered representat...

DECSTR: Learning Goal-Directed Abstract Behaviors using Pre-Verbal Spatial Predicates in Intrinsically Motivated Agents

Intrinsically motivated agents freely explore their environment and set ...

Learning to Query Internet Text for Informing Reinforcement Learning Agents

Generalization to out of distribution tasks in reinforcement learning is...

Situated Dialogue Learning through Procedural Environment Generation

We teach goal-driven agents to interactively act and speak in situated e...

Interpretable Reinforcement Learning with Multilevel Subgoal Discovery

We propose a novel Reinforcement Learning model for discrete environment...

Learning intuitive physics and one-shot imitation using state-action-prediction self-organizing maps

Human learning and intelligence work differently from the supervised pat...

Please sign up or login with your details

Forgot password? Click here to reset