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Natural Language-conditioned reinforcement learning (RL) enables the agentsto follow human instructions. Previous approaches generally implementedlanguage-conditioned RL by providing human instructions in natural language(NL) and training a following policy. In this outside-in approach, the policyneeds to comprehend the NL and manage the task simultaneously. However, theunbounded NL examples often bring much extra complexity for solving concrete RLtasks, which can distract policy learning from completing the task. To ease thelearning burden of the policy, we investigate an inside-out scheme for naturallanguage-conditioned RL by developing a task language (TL) that is task-relatedand unique. The TL is used in RL to achieve highly efficient and effectivepolicy training. Besides, a translator is trained to translate NL into TL. Weimplement this scheme as TALAR (TAsk Language with predicAte Representation)that learns multiple predicates to model object relationships as the TL.Experiments indicate that TALAR not only better comprehends NL instructions butalso leads to a better instruction-following policy that improves 13.4% successrate and adapts to unseen expressions of NL instruction. The TL can also be aneffective task abstraction, naturally compatible with hierarchical RL.

Natural Language-conditioned Reinforcement Learning with Inside-out Task Language Development and Translation