ACKTR

E98477

actor-critic algorithm reinforcement learning algorithm

ACKTR (Actor-Critic using Kronecker-Factored Trust Region) is a reinforcement learning algorithm that combines actor-critic methods with efficient second-order optimization via Kronecker-factored approximations to improve training stability and sample efficiency.

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All labels observed (1)

Label	Occurrences
ACKTR canonical	3

Statements (37)

Predicate	Object
instanceOf	actor-critic algorithm ⓘ reinforcement learning algorithm ⓘ
abbreviationOf	Actor-Critic using Kronecker-Factored Trust Region ⓘ
aimsToImprove	sample efficiency ⓘ training stability ⓘ
approximates	natural gradient ⓘ
basedOn	actor-critic framework ⓘ trust region optimization ⓘ
category	policy gradient method ⓘ value-based method ⓘ
combines	policy gradient learning ⓘ value function estimation ⓘ
comparedWith	A2C ⓘ A3C ⓘ PPO ⓘ TRPO ⓘ
designedFor	policy optimization ⓘ value function learning ⓘ
field	deep reinforcement learning ⓘ
fullName	Actor-Critic using Kronecker-Factored Trust Region ⓘ
hasProperty	on-policy ⓘ sample efficient ⓘ stable training dynamics ⓘ
introducedAs	efficient natural gradient actor-critic method ⓘ
objective	maximize expected cumulative reward ⓘ
optimizationType	second-order method ⓘ
usedIn	Atari benchmarks ⓘ control tasks ⓘ
usesApproximation	Kronecker-factored curvature matrix ⓘ
usesComponent	actor network ⓘ critic network ⓘ
usesGradientInformation	curvature-aware updates ⓘ
usesNaturalGradient	true ⓘ
usesNeuralNetworks	true ⓘ
usesOptimizationMethod	Kronecker-factored approximation ⓘ second-order optimization ⓘ
usesTrustRegion	true ⓘ

Referenced by (3)

Full triples — surface form annotated when it differs from this entity's canonical label.

OpenAI Baselines → implementsAlgorithm → ACKTR ⓘ

A3C → inspiredAlgorithms → ACKTR ⓘ

Stable Baselines → supportsAlgorithm → ACKTR ⓘ