**Actor-critic** algorithms are used in reinforcement learning and combine the advantages of policy-based algorithms like policy gradient estimation with value-based algorithms like Q-learning: one part of the algorithm, the **actor**, suggests an action to perform based on the current environment state parameters, and then a separate part of the algorithm, the **critic**, calculates the value function for this action with respect to the same environment state parameters. See also neural actor-critic.

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- subtype
- has functional building block
- FBB_Behavioural modelling
- has input data type
- IDT_Vector of categorical variables IDT_Binary vector IDT_Vector of quantitative variables
- has internal model
- INM_Markov decision process
- has output data type
- ODT_Classification
- has learning style
- LST_Reinforcement
- has parametricity
- PRM_Parametric
- has relevance
- REL_Relevant
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- sometimes supports
- mathematically similar to