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Reduce, Reuse, Recycle: Categories for Compositional Reinforcement Learning
Artificial Intelligence · Submitted 23 Aug 2024
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim Experimental results support the categorical theory of reinforcement learning by enabling skill reduction, reuse, and recycling when learning complex robotic arm tasks.
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The abstract, sentence by sentence
In reinforcement learning, conducting task composition by forming cohesive, executable sequences from multiple tasks remains challenging. However, the ability to (de)compose tasks is a linchpin in developing robotic systems capable of learning complex behaviors. Yet, compositional reinforcement learning is beset with difficulties, including the high dimensionality of the problem space, scarcity of rewards, and absence of system robustness after task composition. To surmount these challenges, we view task composition through the prism of category theory -- a mathematical discipline exploring structures and their compositional relationships. The categorical properties of Markov decision processes untangle complex tasks into manageable sub-tasks, allowing for strategical reduction of dimensionality, facilitating more tractable reward structures, and bolstering system robustness. Experimental results support the categorical theory of reinforcement learning by enabling skill reduction, reuse, and recycling when learning complex robotic arm tasks.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | In reinforcement learning, conducting task composition by forming cohesive, executable sequences from multiple tasks remains challenging. | background 0.82 | 0.05 | 0.02 | asserted 1.00 |
| s1 | However, the ability to (de)compose tasks is a linchpin in developing robotic systems capable of learning complex behaviors. | background 0.99 | 0.04 | 0.04 | asserted 1.00 |
| s2 | Yet, compositional reinforcement learning is beset with difficulties, including the high dimensionality of the problem space, scarcity of rewards, and absence of system robustness after task composition. | background 0.74 | 0.06 | 0.02 | asserted 1.00 |
| s3 | To surmount these challenges, we view task composition through the prism of category theory -- a mathematical discipline exploring structures and their compositional relationships. | method 0.60 | 0.47 | 0.05 | asserted 0.96 |
| s4 | The categorical properties of Markov decision processes untangle complex tasks into manageable sub-tasks, allowing for strategical reduction of dimensionality, facilitating more tractable reward structures, and bolstering system robustness. | method 0.70 | 0.40 | 0.31 | asserted 1.00 |
| s5 | Experimental results support the categorical theory of reinforcement learning by enabling skill reduction, reuse, and recycling when learning complex robotic arm tasks. | result 0.97 | 0.92 | 0.83 | asserted 1.00 |
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Original abstract
In reinforcement learning, conducting task composition by forming cohesive, executable sequences from multiple tasks remains challenging. However, the ability to (de)compose tasks is a linchpin in developing robotic systems capable of learning complex behaviors. Yet, compositional reinforcement learning is beset with difficulties, including the high dimensionality of the problem space, scarcity of rewards, and absence of system robustness after task composition. To surmount these challenges, we view task composition through the prism of category theory -- a mathematical discipline exploring structures and their compositional relationships. The categorical properties of Markov decision processes untangle complex tasks into manageable sub-tasks, allowing for strategical reduction of dimensionality, facilitating more tractable reward structures, and bolstering system robustness. Experimental results support the categorical theory of reinforcement learning by enabling skill reduction, reuse, and recycling when learning complex robotic arm tasks.
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What it cites · 3 sentences without a confident label
Builds on 2 papers
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Our work builds upon and extends a rich tradition in RL that explores the structure and abstraction in decision processes [ref] and recent developments in applied category theory [ref] .
A Compositional Framework for First-Order Optimization · Related Work · 0.89
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Our work builds upon and extends a rich tradition in RL that explores the structure and abstraction in decision processes [ref] and recent developments in applied category theory [ref] .
2404.02688 · Related Work · 0.89
Uses the method or tool of 1 paper
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To support the categorical formalism through experiments, we tasked RL agents with four distinct manipulation challenges (figure [ref]) using robosuite [ref] . * Lift a block The robot lifts a single block: [equation] * Stack blocks The robot stacks one block on top of another: [equation] * Assemble a nut The robot picks up a nut from the table top and fits it around a pole: [equation] * Pick and place a can The robot retrieves a soda can and places it in a designated bin: [equation] The experiments within the robosuite simulator demonstrate a compositional approach to RL by integrating category theory.
robosuite: A Modular Simulation Framework and Benchmark for Robot Learning · Experiments · 1.00
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