Highway Environment Model for Reinforcement Learning
نویسندگان
چکیده
منابع مشابه
An Environment Model for Nonstationary Reinforcement Learning
Reinforcement learning in nonstationary environments is generally regarded as an important and yet difficult problem. This paper partially addresses the problem by formalizing a subclass of nonsta-tionary environments. The environment model, called hidden-mode Markov decision process (HM-MDP), assumes that environmental changes are always confined to a small number of hidden modes. A mode basic...
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Policy gradient methods have been widely applied in reinforcement learning. For reasons of safety and cost, learning is often conducted using a simulator. However, learning in simulation does not traditionally utilise the opportunity to improve learning by adjusting certain environment variables – state features that are randomly determined by the environment in a physical setting but controlla...
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Simply put, reinforcement learning (RL) is a term used to indicate a large family of dierent algorithms RL that all share two key properties. First, the objective of RL is to learn appropriate behavior through trialand-error experience in a task. Second, in RL, the feedback available to the learning agent is restricted to a reward signal that indicates how well the agent is behaving, but does ...
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ژورنال
عنوان ژورنال: IFAC-PapersOnLine
سال: 2018
ISSN: 2405-8963
DOI: 10.1016/j.ifacol.2018.11.596