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Deep Reinforcement Learning
Reinforcement Learning (RL) is a framework that is used by an agent for decision making. The agent is not necessarily a software entity, such as you might see in video games. Instead, it could be embodied in hardware such as a robot or an autonomous car. An embodied agent is probably the best way to fully appreciate and utilize RL, since a physical entity interacts with the real world and receives responses.
The agent is situated within an environment. The environment has a state that can be partially or fully observable. The agent has a set of actions that it can use to interact with its environment. The result of an action transitions the environment to a new state. A corresponding scalar reward is received after ...
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