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Questions and Answers
What is the term used to describe the Agent using its knowledge to maximize the rewards it receives?
In which Reinforcement Learning approach do we use the known or learned model to plan optimal controls for maximizing rewards?
What is the numerical value received by the Agent from the Environment as a direct response to the Agent's actions?
Which concept, more complex than Policy Gradient, uses second-order optimization?
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'True' or 'False': The policy gradient solves for $max_{ heta} J(\pi_{\theta})$.
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Which term describes the Agent trying many different actions in different states to learn all possibilities and maximize rewards?
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What method is typically used to improve the function approximation in reinforcement learning?
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In Function Approximation, when features are grouped into exhaustive partitions of the input space, what is each partition called?
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What is the concept described as being composed of states, actions, transitions model P, rewards, and the discount factor?
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What is the step size that a filter or mask takes when moving across an array of input pixels in a Convolutional Neural Network (CNN)?
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Which statement accurately describes the relationship between value iteration and policy iteration?
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What could be considered a technique for variance reduction in reinforcement learning?
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Study Notes
Reinforcement Learning
- Reinforcement Learning tasks have no pre-generated training sets and learn "on the fly" by trying many different actions in many different states to learn all available possibilities and maximize overall reward.
Agent's Role
- The Agent needs to try many different actions in many different states to learn all available possibilities and find the path that maximizes its overall reward.
- The Agent uses its knowledge to maximize the rewards it receives, known as Exploitation.
Model-Based RL
- In Model-Based RL, a known or learned model is used to plan the optimal controls in maximizing rewards.
Reward
- A Reward is a numerical value received by the Agent from the Environment as a direct response to the Agent's actions.
Policy Gradient
- The policy gradient solves for max J(Ï€).
- Natural Policy Gradients use a second-order optimization concept which is more accurate but more complex than the Policy Gradient.
Policy Gradient Theorem
- The policy gradient theorem formulates the gradient-based formulation of the Bellman equations.
Value Function
- V(s) can be expressed as the Value Function for finite state MRP in matrix notation.
Convolutional Neural Networks (CNNs)
- In CNN design, the stride is the step size that a filter or mask takes when moving across an array of input pixels.
Function Approximation
- Gradient descent is a method used to improve function approximation.
Policy Gradient
- J(Ï€0) relates the gradient of the policy to the reward function.
- J(πo) relates the objective function to function approximation parameters θ.
Agent's Actions
- The Agent's actions are the things the Agent can do.
Markov Property
- The Markov Property states that the future is independent of the past given the present.
Variance Reduction
- Importance sampling is a technique for variance reduction.
Function Approximation
- In Function Approximation, when features are grouped into exhaustive partitions of the input space, each partition is a patch.
Value Iteration vs Policy Iteration
- Value iteration and policy iteration are both special cases of generalized policy iteration.
Generalized Policy Iteration
- All Generalized Policy Iteration algorithms are synchronous.
Markov Decision Process
- A Markov Decision Process (MDP) is composed of states, actions, the model of transitions P, rewards, and the discount factor.
k-Armed Bandit Problem
- A k-armed bandit problem is a particular type of non-associative problem with evaluative feedback.
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Description
Test your knowledge on how reinforcement learning agents maximize rewards by trying different actions and states to find the best path. Learn about the process of using acquired knowledge to optimize overall reward in reinforcement learning tasks.
