Endogenous Exploration in Reinforcement Learning with Intrinsic Curiosity

arXiv:2609.05650v1 Announce Type: new Abstract: We propose a reinforcement learning framework in which exploration is driven by intrinsic curiosity, designed for scenarios where environments are non-stationary and rewards are sparse, delayed, uninformative, or absent. In our model, action selection is guided by a combination of external rewards and an epistemic motivation mechanism that biases the agent toward structured exploratory directions. The central hypothesis is that effective exploration emerges at intermediate levels of incoherence, while performance degrades under both overly rigid and overly disordered dynamics. To test this idea, we implement the framework on top of a Liquid State Machine (LSM) substrate and evaluate it on two standard benchmarks: the discrete-action LunarLanderv2 and the continuous-control BipedalWalkerv3. The proposed method achieves competitive performance on both tasks relative to established deep RL algorithms, including Proximal Policy Optimization (PPO) and Intrinsic Curiosity Module (ICM). We further show that the curiosity window is not recovered in Active Inference agents under the same analysis, suggesting that the proposed dynamics capture a distinct exploration regime

The text describes a technical advancement in reinforcement learning, but does not make any substantive claims about AI's effect on society. The focus is on the performance of the proposed method in specific benchmarks, without discussing potential implications for human flourishing. Therefore, the item is scored near zero, with low confidence.

The research proposes a reinforcement learning method that improves exploration in challenging environments, which could enhance AI capabilities. However, the described improvements are technical and do not imply direct societal impact. There are potential risks in advanced autonomous systems using curiosity-driven exploration, but evidence here is preliminary and primarily experimental.

This is a narrow algorithmic research contribution on exploration strategies in RL, tested on standard toy benchmarks. It makes no claims about societal deployment, capability escalation, or human impact.

Technical improvement in RL with no clear societal implication

The paper presents a technical advancement in reinforcement learning. It does not discuss societal implications.

SOURCE     arXiv cs.LG (Machine Learning) (tier 1)
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DOCUMENT   7577a088-a494-479e-8654-611afaa495d1
           https://arxiv.org/abs/2609.05650
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EVIDENCE   1 extracted excerpt
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MODEL RUN  5 runs, methodology 1.0.1
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SCORE      +4  (Mixed or uncertain)
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CONFIDENCE 79%