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It uses reinforcement to learn when to switch to an alternative representation method depending on the current observation.
Self-motivation should also encourage learning even when there is little or no external reinforcement to learn and even in the face of obstacles and setbacks to learning.
It is also a positive reinforcement to learn to make a gratitude list every day.
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Previous curiosity-based agents acquired skills by associating intrinsic rewards with world model improvements, and used reinforcement learning to learn how to get these intrinsic rewards.
Agents in our SLMFG are modeled as adaptive learners that use simple reinforcement learning schemes to learn their optimal behavior.
Cooperative Q learning is a reinforcement learning approach to learn the usefulness of some tasks over time in a particular environment.
Reinforcement learning is to learn the optimal policy by a trial-and-error process including perceiving states from the environment, choosing an action according to current states and receiving rewards from the environment.
The goal of reinforcement learning is to learn what actions to select in what situations by learning a value function of situations or "states" [4].
Reinforcement learning uses rewards to learn a successful agent function.
Reinforcement learning allows agents to learn from their past states in order to better perform their following actions and moves.
Each embodied agent uses a model-free Reinforcement Learning (RL) algorithm to learn autonomously to navigate in the virtual environment.
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