The central theme for research in this lab is autonomous agent learning. The agents are robots, models of robots, and interactive video game players. The learning is usually a form of evolutionary computation and almost always some type of computational intelligence. The agents are autonomous in the respect that they can operate on their own and the learning either takes place before operation or can happen during operation using learning systems that are offline from the agent. Most of the learning is for control programs, although some is for the morphology of the robot or control/morphology in combination.
Autonomous Agent Learning Lab
The Autonomous Agent Learning Lab at Connecticut College in New London, CT
- 16 followers
- United States of America
- joconno2@conncoll.edu
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Evo-Sparrow
Evo-Sparrow PublicCode for evolving an LSTM controller for Sparrow Mahjong using CMA-ES
Python 1
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DingoAALL
DingoAALL PublicForked from jaybnash/DingoAALL
Modification of the Dingo Quadruped for research purposes in the Autonomous Agent Learning Lab
Makefile
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CMA-ES-JSON-Evaluator
CMA-ES-JSON-Evaluator PublicIndependent evaluator/evolver written in python using torch
Python
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- core-niching Public
ConnAALL/core-niching’s past year of commit activity - SCOPE-for-Atari Public
ConnAALL/SCOPE-for-Atari’s past year of commit activity - SCOPE-for-Hexapod-Gait-Generation Public
SCOPE uses the Discrete Cosine Transform to compress high-dimensional pose data for hexapod control. By reducing input size from 2700 to 54, it enables faster and more effective evolutionary learning—achieving a 20% performance boost in gait generation.
ConnAALL/SCOPE-for-Hexapod-Gait-Generation’s past year of commit activity - MAML-with-PGX Public
ConnAALL/MAML-with-PGX’s past year of commit activity - MicroRTS Public Forked from Farama-Foundation/MicroRTS
A simple and highly efficient RTS-game-inspired environment for reinforcement learning
ConnAALL/MicroRTS’s past year of commit activity - DingoAALL Public Forked from jaybnash/DingoAALL
Modification of the Dingo Quadruped for research purposes in the Autonomous Agent Learning Lab
ConnAALL/DingoAALL’s past year of commit activity
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