In a fast-paced, dynamic field such as AI, it is crucial to stay well-informed. Even seasoned AI experts understand the need to keep on learning lest they become obsolete. Emerging trends. Algorithmic changes. Technological advancements. These are some of the few things every AI professional should be watching out for. But if you haven’t been keeping an eye on these for whatever reason, don’t worry. We’ve got your covered.
4.2.3 Reinforcement Learning One of the most popular types of machine learning is Reinforcement Learning (RL), which involves training an agent to learn through trial-and-error interactions with an environment. RL is an iterative process, where the agent receives feedback from the environment in the form of rewards or penalties and uses that feedback to learn to make better decisions in the future. At the core of RL is the concept of an agent, which is a program that interacts with an environment to achieve a specific goal. The agent receives feedback from the environment in the form of a reward or penalty, which is used to update the agent's policy, or the set of rules it uses to make decisions. The goal of the agent is to learn a policy that maximizes the cumulative reward over time. One of the main advantages of RL is its ability to handle complex, dy
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RL algorithms can learn to perform tasks in environments where the optimal policy is unknown or changes over time. This makes RL well-suited for a wide range of applications, including robotics, game playing, and autonomous vehicles. One of the key challenges in RL is balancing exploration and exploitation. The agent must explore the environment to learn the optimal policy, but it must also exploit its current knowledge to maximize rewards. This trade-off can be addressed using various exploration strategies, such as -greedy, which balances exploration and exploitation by selecting a random action with probability and the optimal action with probability 1-. Another challenge in RL is the credit assignment problem, which involves determining which actions led to a particular reward or penalty. This is especially difficult in environments with delayed rewards, where the consequences of an action may not be realized until man
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To address this, RL algorithms use a technique called temporal-difference learning, which updates the agent's policy based on the difference between the predicted and actual rewards.
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THE BEGINNERS GUIDE TO ARTIFICIAL INTELLIGENCE (AI) Frank A Dartey (AIWeblog.com) 2023 THE BEGINNERS GUIDE TO ARTIFICIAL INTELLIGENCE (AI) Frank A Dartey (AIWeblog.com)
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