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ARXIV:2604.25369 · REINFORCEMENT LEARNING · SUBMITTED 29 APR · 02:31 UTC · FRESHNESS STALE
ARXIV:2604.25369REINFORCEMENT LEARNINGSUBMITTED 29 APR · 02:31 UTCFRESHNESS STALEQuentin Vacher · Nicolas Beuve · Mickaël Dardaillon · Karol Desnos · arXiv
A genetic programming algorithm for multi-task reinforcement learning in continuous control environments with interpretable decision flows.
Opportunity summary
Pain A genetic programming algorithm for multi-task reinforcement learning in continuous control environments with interpretable decision flows.
Evidence 0 refs | 3 sources | 50% coverage
Blocker Evidence unverified
A genetic programming algorithm for multi-task reinforcement learning in continuous control environments with interpretable decision flows. Reinforcement Learning (RL), inspired by human behavior, is a great example, as it involves developing specific behaviours for…
Over the past few decades, machine learning has been widely used to learn complex tasks. Reinforcement Learning (RL), inspired by human behavior, is a great example, as it involves developing specific behaviours for specific…
ScienceToStartup currently rates this 4.0/10 on the public viability pass. Recently, the MAPLE algorithm has been proposed, as another GP algorithm that achieves high results in single task continuous RL environments. Code availability is…
Reinforcement Learning moved forward this cycle; last verified April 2026. Public score 4.0/10. Production flags indicate code availability.
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A genetic programming algorithm for multi-task reinforcement learning in continuous control environments with interpretable decision flows.
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10.48550/arXiv.2604.25369A genetic programming algorithm for multi-task reinforcement learning in continuous control environments with interpretable decision flows.
Abstract
Over the past few decades, machine learning has been widely used to learn complex tasks. Reinforcement Learning (RL), inspired by human behavior, is a great example, as it involves developing specific behaviours for specific tasks. To further challenge algorithms, Multi-Task RL (MTRL) environments have been introduced, requiring a single model to learn multiple behaviors. The Tangled Program Graph (TPG) algorithm is a Genetic Programming (GP) algorithm designed for discrete MTRL environments. Recently, the MAPLE algorithm has been proposed, as another GP algorithm that achieves high results in single task continuous RL environments. A variation of the TPG is proposed alongside MAPLE, named Multi-Action TPG (MATPG) that aggregates MAPLE agents, and creates a control flow to activate them. Initially tested on single task RL environments only, MATPG achieved similar results to MAPLE. In this work, we present a new benchmark based on the MuJoCo Half Cheetah from Gymnasium. This benchmark features five distinct obstacles that are randomly positioned in front of the agent, each of which demands a unique behavior. This benchmark serves as a use case for MATPG, to prove its ability as a GP solution for continuous MTRL environments. Our experiments demonstrate its superiority in this multi-task use case when combined with lexicase selection. Furthermore, we examine the interpretability of the evolved graph, revealing that the decision flow of the model is fully interpretable.
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PROBLEM
A genetic programming algorithm for multi-task reinforcement learning in continuous control environments with interpretable decision flows. Reinforcement Learning (RL), inspired by human behavior, is a great example, as it involves developing specific behaviours for specific tas...
METHOD
Over the past few decades, machine learning has been widely used to learn complex tasks. Reinforcement Learning (RL), inspired by human behavior, is a great example, as it involves developing specific behaviours for specific tasks.
RESULT
ScienceToStartup currently rates this 4.0/10 on the public viability pass. Recently, the MAPLE algorithm has been proposed, as another GP algorithm that achieves high results in single task continuous RL environments. Code availability is flagged in the production record; the pu...
WHY NOW
Reinforcement Learning moved forward this cycle; last verified April 2026. Public score 4.0/10. Production flags indicate code availability.
Abstract-backed public claims while anchored extraction refreshes.
A genetic programming algorithm for multi-task reinforcement learning in continuous control environments with interpretable decision flows. Reinforcement Learning (RL), inspired by human behavior, is a great example, as it involves developing specific behaviours for specific tasks.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Over the past few decades, machine learning has been widely used to learn complex tasks. Reinforcement Learning (RL), inspired by human behavior, is a great example, as it involves developing specific behaviours for specific tasks.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 4.0/10 on the public viability pass. Recently, the MAPLE algorithm has been proposed, as another GP algorithm that achieves high results in single task continuous RL environments. Code availability is flagged in the production record; the public repository link still needs proof alignment.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Reinforcement Learning moved forward this cycle; last verified April 2026. Public score 4.0/10. Production flags indicate code availability.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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A genetic programming algorithm for multi-task reinforcement learning in continuous control environments with interpretable decision flows.
Segment
Reinforcement Learning
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Commercial read
4.0/10 public viability
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reason
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proof status
unverified
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next verification path
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stale
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passport absent
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Artifact maturity
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stale
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Technical feasibility
partial
Current read
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Gaps
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Run minimal reproduction from the Build Passport prototype path.
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0 references, 3 sources, 50% evidence coverage.
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missing
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Write integration checklist from prototype path and target workflow.
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Classify regulatory flags before commercialization planning.
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