Cogito, Ergo Ludo: An Agent that Learns to Play by Reasoning and
  Planning
The pursuit of artificial agents that can learn to master complex
environments has led to remarkable successes, yet prevailing deep reinforcement
learning methods often rely on immense experience, encoding their knowledge
opaquely within neural network weights. We propose a different paradigm, one in
which an agent learns to play by reasoning and planning. We introduce Cogito,
ergo ludo (CEL), a novel agent architecture that leverages a Large Language
Model (LLM) to build an explicit, language-based understanding of its
environment's mechanics and its own strategy. Starting from a tabula rasa state
with no prior knowledge (except action set), CEL operates on a cycle of
interaction and reflection. After each episode, the agent analyzes its complete
trajectory to perform two concurrent learning processes: Rule Induction, where
it refines its explicit model of the environment's dynamics, and Strategy and
Playbook Summarization, where it distills experiences into an actionable
strategic playbook. We evaluate CEL on diverse grid-world tasks (i.e.,
Minesweeper, Frozen Lake, and Sokoban), and show that the CEL agent
successfully learns to master these games by autonomously discovering their
rules and developing effective policies from sparse rewards. Ablation studies
confirm that the iterative process is critical for sustained learning. Our work
demonstrates a path toward more general and interpretable agents that not only
act effectively but also build a transparent and improving model of their world
through explicit reasoning on raw experience.
68db56bdd2bf1f4b15ec752d
Sai Wang
68db56bdd2bf1f4b15ec752e
Yu Wu
68db56bdd2bf1f4b15ec752f
Zhongwen Xu
claimed_verified
2025-09-30T06:00:41.196Z
672db76fa34d64e774fc42c9
/avatars/be529f4bfcc40514697facbe8e874735.svg
Zhongwen Xu
user
zhongwenxu
Cogito, Ergo Ludo: An Agent that Learns to Play by Reasoning and
  Planning
The pursuit of artificial agents that can learn to master complex
environments has led to remarkable successes, yet prevailing deep reinforcement
learning methods often rely on immense experience, encoding their knowledge
opaquely within neural network weights. We propose a different paradigm, one in
which an agent learns to play by reasoning and planning. We introduce Cogito,
ergo ludo (CEL), a novel agent architecture that leverages a Large Language
Model (LLM) to build an explicit, language-based understanding of its
environment's mechanics and its own strategy. Starting from a tabula rasa state
with no prior knowledge (except action set), CEL operates on a cycle of
interaction and reflection. After each episode, the agent analyzes its complete
trajectory to perform two concurrent learning processes: Rule Induction, where
it refines its explicit model of the environment's dynamics, and Strategy and
Playbook Summarization, where it distills experiences into an actionable
strategic playbook. We evaluate CEL on diverse grid-world tasks (i.e.,
Minesweeper, Frozen Lake, and Sokoban), and show that the CEL agent
successfully learns to master these games by autonomously discovering their
rules and developing effective policies from sparse rewards. Ablation studies
confirm that the iterative process is critical for sustained learning. Our work
demonstrates a path toward more general and interpretable agents that not only
act effectively but also build a transparent and improving model of their world
through explicit reasoning on raw experience.
CEL, a novel agent architecture using a Large Language Model, learns to master complex environments through explicit reasoning and planning, achieving success in diverse grid-world tasks with sparse rewards.
deep reinforcement learning Large Language Model Cogito ergo ludo CEL tabula rasa interaction and reflection Rule Induction Strategy and Playbook Summarization Minesweeper Frozen Lake Sokoban explicit reasoning transparent model