Composition

Compositional Instruction Following with Language Models and Reinforcement Learning
Combining reinforcement learning with language grounding is challenging as the agent needs to explore the environment while …
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End-to-End Learning to Follow Language Instructions with Compositional Policies
We develop an end-to-end model for learning to follow language instructions with compositional policies. Our model combines large …
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Skill Machines: Temporal Logic Composition in Reinforcement Learning
A major challenge in reinforcement learning is specifying tasks in a manner that is both interpretable and verifiable. One common …
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Facilitating Safe Sim-to-Real through Simulator Abstraction and Zero-shot Task Composition
Simulators are a fundamental part of training robots to solve complex control and navigation tasks. This is due to the speed and safety …
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Skill Machines: Temporal Logic Composition in Reinforcement Learning
A major challenge in reinforcement learning is specifying tasks in a manner that is both interpretable and verifiable. One common …
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Learning to Follow Language Instructions with Compositional Policies
We propose a framework that learns to execute natural language instructions in an environment consisting of goal-reaching tasks that …
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Composing Value Functions in Reinforcement Learning
An important property for lifelong-learning agents is the ability to combine existing skills to solve new unseen tasks. In general, …
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Will it Blend? Composing Value Functions in Reinforcement Learning
An important property for lifelong-learning agents is the ability to combine existing skills to solve unseen tasks. In general, …
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Hierarchy Through Composition with Multitask LMDPs
Hierarchical architectures are critical to the scalability of reinforcement learning methods. Most current hierarchical frameworks …
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