{"slug": "endogenous-exploration-in-reinforcement-learning-with-intrinsic-curiosity", "title": "Endogenous Exploration in Reinforcement Learning with Intrinsic Curiosity", "summary": "A reinforcement learning framework that drives exploration through intrinsic curiosity achieved competitive performance against Proximal Policy Optimization (PPO) and the Intrinsic Curiosity Module (ICM) on the LunarLanderv2 and BipedalWalkerv3 benchmarks, according to arXiv paper 2609.05650v1. The framework, built on a Liquid State Machine (LSM) substrate, combines external rewards with an epistemic motivation mechanism that biases agents toward structured exploratory directions, with the authors hypothesizing that effective exploration emerges at intermediate levels of incoherence. The authors report the curiosity window was not recovered in Active Inference agents under the same analysis, suggesting the proposed dynamics capture a distinct exploration regime.", "body_md": "arXiv:2609.05650v1 Announce Type: new \nAbstract: 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", "url": "https://wpnews.pro/news/endogenous-exploration-in-reinforcement-learning-with-intrinsic-curiosity", "canonical_source": "https://arxiv.org/abs/2609.05650", "published_at": "2026-09-10 04:00:00+00:00", "updated_at": "2026-09-10 04:22:53.953374+00:00", "lang": "en", "topics": ["machine-learning", "ai-research", "robotics"], "entities": ["Liquid State Machine", "LunarLanderv2", "BipedalWalkerv3", "Proximal Policy Optimization", "Intrinsic Curiosity Module", "Active Inference", "arXiv"], "alternates": {"html": "https://wpnews.pro/news/endogenous-exploration-in-reinforcement-learning-with-intrinsic-curiosity", "markdown": "https://wpnews.pro/news/endogenous-exploration-in-reinforcement-learning-with-intrinsic-curiosity.md", "text": "https://wpnews.pro/news/endogenous-exploration-in-reinforcement-learning-with-intrinsic-curiosity.txt", "jsonld": "https://wpnews.pro/news/endogenous-exploration-in-reinforcement-learning-with-intrinsic-curiosity.jsonld"}}