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.

Statements (37)

Predicate	Object
instanceOf	actor-critic algorithm → reinforcement learning algorithm →
abbreviationOf	Actor-Critic using Kronecker-Factored Trust Region NERFINISHED →
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 NERFINISHED → PPO NERFINISHED → TRPO NERFINISHED →
designedFor	policy optimization → value function learning →
field	deep reinforcement learning →
fullName	Actor-Critic using Kronecker-Factored Trust Region NERFINISHED →
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)

Subject (surface form when different)	Predicate
OpenAI Baselines →	implementsAlgorithm
A3C →	inspiredAlgorithms
Stable Baselines →	supportsAlgorithm