Beyond Trial and Error: How Neural Networks Elevate Deep Reinforcement Learning.

Author(s): Kapardhi kannekanti

Originally published on Towards AI.

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At the foundation of this lies in the Multi-Arm Bandit Problem, a classic dilemma in Probability Theory and Machine Learning.

The fundamental “exploration vs. exploitation” trade-off. Should the gambler keep playing the machine that has paid out the most so far (exploit), or try other machines to gather more information (explore)?

Algorithms like ε-greedy, Upper Confidence Bound (UCB), and Thompson Sampling were developed to balance exploration and exploitation effectively.

While multi-armed bandits provided a solid foundation, many real-world scenarios require considering additional information or context when making decisions.Contextual bandits expanded the classic bandit problem by introducing context or features associated with each decision.Now, before each “pull,” the algorithm receives some contextual information that can inform its choice. For instance, in a news recommendation system, the context might be the user’s browsing history or time of day.

Algorithms like contextual Thompson Sampling emerged to tackle these more complex scenarios.

Deep Reinforcement Learning combines the sequential decision-making framework of reinforcement learning with the representational power of deep neural networks. This fusion allows for learning in complex environments with high-dimensional state spaces, like visual input from video games or robotic sensors.

Key Components of DRL includes:

State representation: Neural… Read the full blog for free on Medium.

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