Self-Improving Logic from Experimental Observations
self-improving logic;reinforcement learning;hierarchical learning;second-order logic;markov decision process;group representations;tokenisation;model-based learning;dynamics models;machine understanding;unsupervised learning;computational learning theory;non-convex optimisation;wahba's problem;skill-based reinforcement learning;skills pruning;transfer learning
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Submission Desk Rejected by Program Chairs
Learned representations of actions in topological groups as an humanly-understandable alternative to neural networks to efficiently structure a model-based autonomous agent's dynamics prediction and planning within a Markov Decision Process.
@misc{
pont2024selfimproving,
title={Self-Improving Logic from Experimental Observations},
author={Max PONT},
year={2024},
url={https://openreview.net/forum?id=GnCw9lCPs9}
}
Learning relevant, transferable representations of actions to drive model-based reinforcement learning processes stands as a major challenge in robotics on the path to general-purpose autonomous agents, equivalent in their reasoning power to Large Language Models. To this end, we introduce a novel framework which allows autonomous agents to learn how to represent their actions as high-dimensional rotations over the system’s observations. We then show how such representations may be considered optimal under the assumption that actions are distance-preserving, and present how these representations of low-level actions can be composed to represent sequences of actions and allow for multi-scale hierarchical learning and long-horizon planning. We finally discuss schemes to compare such representations in order to allow for a better informed transfer of skills across tasks and better understand the agent’s behaviour, before conducting experiments using a modified TD-MPC2 agent to better quantify $in$ $concreto$ the limitations of our framework.
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Max PONT
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self-improving logic reinforcement learning hierarchical learning second-order logic markov decision process group representations tokenisation model-based learning dynamics models machine understanding unsupervised learning computational learning theory non-convex optimisation wahba's problem skill-based reinforcement learning skills pruning transfer learning
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pont|selfimproving_logic_from_experimental_observations
/pdf/24eeff2bddb587ff8b77e23fdcc101bda98a858f.pdf
interpretability and explainable AI
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Self-Improving Logic from Experimental Observations
ICLR 2025 Conference Desk Rejected Submission
ICLR.cc/2025/Conference/Desk_Rejected_Submission