What are the key differences between reinforcement learning and other types of machine learning, such as supervised and unsupervised learning?
Reinforcement learning (RL) is a subfield of machine learning that focuses on how agents should take actions in an environment to maximize cumulative reward. This approach is fundamentally different from supervised and unsupervised learning, which are the other primary paradigms in machine learning. To understand the key differences between these types of learning, it is
What is the difference between model-free and model-based reinforcement learning, and how do each of these approaches handle the decision-making process?
In the domain of reinforcement learning (RL), there exists a fundamental distinction between model-free and model-based approaches, each offering unique methodologies for the decision-making process. Model-free reinforcement learning refers to methods that learn policies or value functions directly from interactions with the environment without constructing an explicit model of the environment's dynamics. This approach relies
- Published in Artificial Intelligence, EITC/AI/ARL Advanced Reinforcement Learning, Deep reinforcement learning, Planning and models, Examination review
What role do the actor and critic play in actor-critic methods, and how do their update rules help in reducing the variance of policy gradient estimates?
In the domain of advanced reinforcement learning, particularly within the context of deep reinforcement learning, actor-critic methods represent a significant class of algorithms designed to address some of the challenges associated with policy gradient techniques. To fully grasp the role of the actor and critic in these methods, it is essential to delve into the
How do policy gradient methods optimize the policy, and what is the significance of the gradient of the expected reward with respect to the policy parameters?
Policy gradient methods are a class of algorithms in reinforcement learning that aim to directly optimize the policy, which is a mapping from states to actions, by adjusting the parameters of the policy function in a way that maximizes the expected reward. These methods are distinct from value-based methods, which focus on estimating the value
- Published in Artificial Intelligence, EITC/AI/ARL Advanced Reinforcement Learning, Deep reinforcement learning, Policy gradients and actor critics, Examination review
What is the fundamental difference between exploration and exploitation in the context of reinforcement learning?
In the context of reinforcement learning (RL), the concepts of exploration and exploitation represent two fundamental strategies that an agent employs to make decisions and learn optimal policies. These strategies are pivotal to the agent's ability to maximize cumulative rewards over time, and understanding the distinction between them is crucial for designing effective RL algorithms.
How are the policy gradients used?
Policy gradient methods are a class of algorithms in reinforcement learning that optimize the policy directly. In reinforcement learning, a policy is a mapping from states of the environment to actions to be taken when in those states. The objective of policy gradient methods is to find the optimal policy that maximizes the expected cumulative