Reinforcement learning (RL) and deep reinforcement learning (DRL) are related concepts, but they differ in the techniques and approaches used in the learning process. This interesting topic came to us from Research Rebellion in their article, “What Is The Difference Between Reinforcement Learning And Deep Reinforcement Learning?“
RL and DRL are two exciting subfields of machine learning that have garnered significant attention in recent years. Both approaches involve training agents to make sequential decisions to maximize cumulative rewards, but they differ in the techniques and tools they use to achieve this goal.
RL is a powerful tool for solving problems where an agent interacts with an environment. The agent learns a policy – a mathematical strategy for selecting actions – that maximizes the cumulative reward over time. This implies that the agent takes actions, observes the resulting state and reward, and uses the results as feedback for adjusting its strategy.
DRL is an extension of traditional RL that incorporates deep neural networks to handle high-dimensional state spaces. The introduction of neural networks in DRL allows agents to learn directly from raw sensory data, such as images or sensor readings, making it suitable for more complex and real-world applications.
The choice between RL and DRL depends on the complexity of the problem and the nature of the input data.
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