Tag: reinforcement learning

Policy Gradient Methods

Policy Gradient Methods

Policy Gradient Methods Reinforcement Learning Delving into the realm of reinforcement learning, policy gradient methods stand out as a strategy that directly tweaks the policy, mapping states to actions, to enhance performance. Unlike methods that estimate value functions, policy gradient methods adjust the policy parameters (ฮธ) by ascending along the gradient of the expected reward. This approach can be likened to climbing a mountain, feeling the slope underfoot and taking steps upwards, always aiming for the peak where the reward is maximized. Unveiling Policy Optimization At the core of policy gradient methods lies policy optimization, aiming

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Q-Learning and Deep Q Networks (DQNs)

Q-Learning and Deep Q Networks (DQNs)

What is Q-learning and deep Q network? In the vast landscape of artificial intelligence, reinforcement learning stands out as a powerful paradigm, enabling agents to learn optimal behavior through trial and error. Among its arsenal of techniques, Q-learning and Deep Q Networks (DQNs) emerge as beacons of innovation, illuminating paths to navigate complex decision spaces with remarkable efficiency. Basics of Q-Learning Q-learning, a fundamental algorithm in reinforcement learning, embarks on a journey where an agent, akin to an intrepid explorer, traverses a labyrinth of decisions and consequences. At its core lies the Q-table, a map of

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Introduction to Reinforcement Learning

Introduction to Reinforcement Learning

The Genesis of Learning from Interaction In the vast and intricate world of artificial intelligence, the concept of learning through interaction stands as a cornerstone, paving the way for systems that not only understand but adapt. This foundational premise is what we explore under the umbrella of reinforcement learning (RL). At its core, RL is a paradigm where agents learn to make decisions by interacting with their environment. This interaction is governed by the principle of trial and error, where actions lead to rewards or penalties, guiding the agent towards optimal behavior. The Pillars of Reinforcement

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