Understanding Actor-critic: Definition, Explanations, Examples & Code
Actor-critic is a temporal difference algorithm used in reinforcement learning.
It consists of two networks: the actor, which decides which action to take, and the critic, which evaluates the action produced by the actor by computing the value function and informs the actor how good the action was and how it should adjust.
In simple terms, the actor-critic is a temporal difference version of policy gradient. The learning of
Understanding Asynchronous Advantage Actor-Critic: Definition, Explanations, Examples & Code
The Asynchronous Advantage Actor-Critic (A3C) algorithm is a deep reinforcement learning method that uses multiple independent neural networks to generate trajectories and update parameters asynchronously. It involves two models: an actor, which decides which action to take, and a critic, which estimates the value of taking that action. A3C is abbreviated as A3C and falls under the category of deep lear
Understanding Policy Gradients: Definition, Explanations, Examples & Code
Policy Gradients (PG) is an optimization algorithm used in artificial intelligence and machine learning, specifically in the field of reinforcement learning. This algorithm operates by directly optimizing the policy the agent is using, without the need for a value function. The agent's policy is typically parameterized by a neural network, which is trained to maximize expected return.
Policy Gradients: Introduction
Understanding State-Action-Reward-State-Action: Definition, Explanations, Examples & Code
SARSA (State-Action-Reward-State-Action) is a temporal difference on-policy algorithm used in reinforcement learning to train a Markov decision process model on a new policy. This algorithm falls under the category of reinforcement learning, which focuses on how an agent should take actions in an environment to maximize a cumulative reward signal.
State-Action-Reward-State-Action: Introduction
Domain