Context-switching and adaptation: Brain-inspired mechanisms for handling environmental changes
E Chalmers, EB Contreras, B Robertson… - … Joint Conference on …, 2016 - ieeexplore.ieee.org
Reinforcement learning (RL) allows an intelligent agent to learn optimal behavior as it
interacts with its environment. Conventional model-based RL algorithms learn rapidly, but …
interacts with its environment. Conventional model-based RL algorithms learn rapidly, but …
[PDF][PDF] Context-Switching and Adaptation: Brain-Inspired Mechanisms for Handling Environmental Changes
E Chalmers, EB Contreras, B Robertson, A Luczak… - people.uleth.ca
Reinforcement learning (RL) allows an intelligent agent to learn optimal behavior as it
interacts with its environment. Conventional model-based RL algorithms learn rapidly, but …
interacts with its environment. Conventional model-based RL algorithms learn rapidly, but …
[PDF][PDF] Context-Switching and Adaptation: Brain-Inspired Mechanisms for Handling Environmental Changes
E Chalmers, EB Contreras, B Robertson, A Luczak… - researchgate.net
Reinforcement learning (RL) allows an intelligent agent to learn optimal behavior as it
interacts with its environment. Conventional model-based RL algorithms learn rapidly, but …
interacts with its environment. Conventional model-based RL algorithms learn rapidly, but …
[PDF][PDF] Context-Switching and Adaptation: Brain-Inspired Mechanisms for Handling Environmental Changes
E Chalmers, EB Contreras, B Robertson, A Luczak… - people.uleth.ca
Reinforcement learning (RL) allows an intelligent agent to learn optimal behavior as it
interacts with its environment. Conventional model-based RL algorithms learn rapidly, but …
interacts with its environment. Conventional model-based RL algorithms learn rapidly, but …