actor-critic method

C15711

concept

An actor-critic method is a reinforcement learning approach that combines a policy model (actor) that selects actions with a value model (critic) that evaluates those actions to improve the policy.

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All labels observed (6)

Label	Occurrences
off-policy reinforcement learning algorithm	3
actor-critic method canonical	2
goal-conditioned reinforcement learning method	1
on-policy reinforcement learning method	1
policy gradient method component	1
reinforcement learning paper	1

Description generation (CDg)

The one-sentence description above was generated by prompting gpt-5.1 with the class name and this instruction.

Instruction

generate a one-sentence description for a given conceptual class.
# Response Format
Return only the sentence: "Description: [one-sentence description of the conceptional class]"

Input

Class: actor-critic method

Generated description

An actor-critic method is a reinforcement learning approach that combines a policy model (actor) that selects actions with a value model (critic) that evaluates those actions to improve the policy.

Instances (8)

Instance	Via concept surface
Generalized Advantage Estimation	policy gradient method component
SAC surface form: Soft Actor-Critic	off-policy reinforcement learning algorithm
TD3	off-policy reinforcement learning algorithm
REINFORCE	on-policy reinforcement learning method
Asynchronous Methods for Deep Reinforcement Learning	reinforcement learning paper
Actor-Critic using Kronecker-Factored Trust Region	—
Hindsight Policy Gradients	goal-conditioned reinforcement learning method
A2C	—