richard-s-sutton
OfficialThink like Sutton to design continual RL systems.
Education & Research#agent-design#reinforcement-learning#ai-alignment#continual-learning#bitter-lesson#reward-hypothesis#decentralized-cooperation
AuthorK-Dense-AI
Version1.0.0
Installs0
System Documentation
What problem does it solve?
Provides a rigorous, computation-first lens drawn from Richard S. Sutton to guide evaluation and design of reinforcement learning agents, continual-learning systems, and AI alignment discussions.
Core Features & Use Cases
- On-demand reasoning about RL architectures using Sutton's Core Principles, The Bitter Lesson, reward hypothesis, and the Common Model of the Intelligent Agent.
- Tools for evaluating agent-environment boundaries, decentralized cooperation vs centralized control, and design-time vs runtime decisions.
- Use cases include architecture critique, long-horizon AI prognostication, and runtime-learning system design across robotics, autonomy, and decision-making domains.
Quick Start
Provide Sutton-inspired evaluation of an RL system by emphasizing runtime learning and goal-directed optimization.
Dependency Matrix
Required Modules
None requiredComponents
references
💻 Claude Code Installation
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Please help me install this Skill: Name: richard-s-sutton Download link: https://github.com/K-Dense-AI/mimeographs/archive/main.zip#richard-s-sutton Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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