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ARXIV:2601.21391 · REINFORCEMENT LEARNING · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2601.21391REINFORCEMENT LEARNINGSUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEarXiv
A reinforcement learning framework improving exploration in sparse-reward environments via intrinsic rewards.
Opportunity summary
Pain A reinforcement learning framework improving exploration in sparse-reward environments via intrinsic rewards.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
A reinforcement learning framework improving exploration in sparse-reward environments via intrinsic rewards. However, when rewards are sparse, naive exploration strategies, like noise injection, are often insufficient.
Exploration is essential in reinforcement learning as an agent relies on trial and error to learn an optimal policy. However, when rewards are sparse, naive exploration strategies, like noise injection, are often insufficient.
ScienceToStartup currently rates this 6.0/10 on the public viability pass. Our algorithm -- intrinsic reward policy optimization (IRPO) -- achieves this by using a surrogate policy gradient that provides a more informative learning signal…
Reinforcement Learning moved forward this cycle; last verified April 2026. Public score 6.0/10.
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A reinforcement learning framework improving exploration in sparse-reward environments via intrinsic rewards.
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10.48550/arXiv.2601.21391A reinforcement learning framework improving exploration in sparse-reward environments via intrinsic rewards.
Abstract
Exploration is essential in reinforcement learning as an agent relies on trial and error to learn an optimal policy. However, when rewards are sparse, naive exploration strategies, like noise injection, are often insufficient. Intrinsic rewards can also provide principled guidance for exploration by, for example, combining them with extrinsic rewards to optimize a policy or using them to train subpolicies for hierarchical learning. However, the former approach suffers from unstable credit assignment, while the latter exhibits sample inefficiency and sub-optimality. We propose a policy optimization framework that leverages multiple intrinsic rewards to directly optimize a policy for an extrinsic reward without pretraining subpolicies. Our algorithm -- intrinsic reward policy optimization (IRPO) -- achieves this by using a surrogate policy gradient that provides a more informative learning signal than the true gradient in sparse-reward environments. We demonstrate that IRPO improves performance and sample efficiency relative to baselines in discrete and continuous environments, and formally analyze the optimization problem solved by IRPO. Our code is available at https://github.com/Mgineer117/IRPO.
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PROBLEM
A reinforcement learning framework improving exploration in sparse-reward environments via intrinsic rewards. However, when rewards are sparse, naive exploration strategies, like noise injection, are often insufficient.
METHOD
Exploration is essential in reinforcement learning as an agent relies on trial and error to learn an optimal policy. However, when rewards are sparse, naive exploration strategies, like noise injection, are often insufficient.
RESULT
ScienceToStartup currently rates this 6.0/10 on the public viability pass. Our algorithm -- intrinsic reward policy optimization (IRPO) -- achieves this by using a surrogate policy gradient that provides a more informative learning signal than the true gradient in sparse-reward...
WHY NOW
Reinforcement Learning moved forward this cycle; last verified April 2026. Public score 6.0/10.
Abstract-backed public claims while anchored extraction refreshes.
A reinforcement learning framework improving exploration in sparse-reward environments via intrinsic rewards. However, when rewards are sparse, naive exploration strategies, like noise injection, are often insufficient.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Exploration is essential in reinforcement learning as an agent relies on trial and error to learn an optimal policy. However, when rewards are sparse, naive exploration strategies, like noise injection, are often insufficient.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 6.0/10 on the public viability pass. Our algorithm -- intrinsic reward policy optimization (IRPO) -- achieves this by using a surrogate policy gradient that provides a more informative learning signal than the true gradient in sparse-reward environments.
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 6.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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A reinforcement learning framework improving exploration in sparse-reward environments via intrinsic rewards.
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