Actor-Critic
RL architecture with two components: an actor (policy) that selects actions and a critic (value function) that evaluates them.
Your route here
11 stops · basics first
- Machine Learning ✓ understood
Building systems that learn patterns from data instead of following hand-written rules, getting better at a task as they see more examples.
- Reinforcement Learning ✓ understood
Learning through interaction with an environment, receiving rewards or penalties to learn optimal behavior policies.
- Agent ✓ understood
In RL, the learner or decision-maker that takes actions in an environment to maximize cumulative reward.
- Policy ✓ understood
A strategy or mapping from states to actions that defines the agent's behavior in reinforcement learning.
- Dataset ✓ understood
A collection of data examples used for training, validating, or testing machine learning models.
- Training ✓ understood
The process of fitting a model to data by repeatedly measuring how wrong its outputs are and adjusting its parameters to reduce that error.
- Loss Function ✓ understood
A function that scores how wrong a model's prediction is as a single number, which training then works to make as small as possible.
- Gradient Descent ✓ understood
An optimization method that repeatedly moves a model's parameters a small step in the direction that most reduces the loss.
- Policy Gradient ✓ understood
RL methods that directly optimize the policy by computing gradients of expected reward with respect to policy parameters.
- Reward ✓ understood
A scalar feedback signal indicating how good an action was, used to train reinforcement learning agents.
- Value Function ✓ understood
A function estimating expected cumulative reward from a state (state-value) or state-action pair (action-value/Q-value).
- Actor-Critic · you are here ✓ understood