{"slug": "ridge-state-conditioned-reward-blending-for-behavioral-coverage-in-deep-rl-game", "title": "RIDGE: State-Conditioned Reward Blending for Behavioral Coverage in Deep RL Game Agents", "summary": "A single RIDGE agent trained with state-conditioned reward blending acquired both branches of Crafter's wood-tier crafting/combat split, a joint outcome no fixed-persona baseline achieved, according to an arXiv paper (2611.05601v1). RIDGE combines a state-conditioned blender that computes softmax-normalized sigmoid weights from slow-moving state variables with a multi-head critic that trains one value head per persona on its own stationary signal, avoiding the PPO degradation seen under non-stationary rewards. The authors report results across n=3 seeds per condition and describe the work as demonstrations of the blending mechanism's dynamics, with larger multi-seed sweeps left as future work.", "body_md": "arXiv:2611.05601v1 Announce Type: new \nAbstract: A deep RL agent trained on a fixed persona reward plays one way: an Explorer keeps exploring while starving, a Survivor hoards on an empty map. Since each persona fixes its own reward, automated playtesting trains N agents for N styles. Changing reward composition mid-episode runs into the switching-stability dilemma: PPO degrades under non-stationary rewards. We present RIDGE, a single-agent architecture that sidesteps the dilemma with two design choices: a state-conditioned blender that computes softmax-normalized sigmoid weights from slow-moving state variables, and a multi-head critic that trains one value head per persona on its own stationary signal. On Crafter with four personas, one \\ridge agent acquires both branches of the wood-tier crafting/combat split that no fixed-persona baseline solves jointly, and a sharpness ablation confirms state-conditioning drives the gain. We position these results as demonstrations of the blending mechanism's dynamics across n=3 seeds per condition; larger multi-seed sweeps remain future work.", "url": "https://wpnews.pro/news/ridge-state-conditioned-reward-blending-for-behavioral-coverage-in-deep-rl-game", "canonical_source": "https://arxiv.org/abs/2611.05601", "published_at": "2026-11-05 05:00:00+00:00", "updated_at": "2026-11-05 05:22:30.986692+00:00", "lang": "en", "topics": ["machine-learning", "ai-research", "ai-agents", "artificial-intelligence"], "entities": ["RIDGE", "Crafter", "PPO", "arXiv"], "also_reported_by": [], "alternates": {"html": "https://wpnews.pro/news/ridge-state-conditioned-reward-blending-for-behavioral-coverage-in-deep-rl-game", "markdown": "https://wpnews.pro/news/ridge-state-conditioned-reward-blending-for-behavioral-coverage-in-deep-rl-game.md", "text": "https://wpnews.pro/news/ridge-state-conditioned-reward-blending-for-behavioral-coverage-in-deep-rl-game.txt", "jsonld": "https://wpnews.pro/news/ridge-state-conditioned-reward-blending-for-behavioral-coverage-in-deep-rl-game.jsonld"}}