concept

Actor-Critic

Actor-Critic is a reinforcement learning algorithm that combines value-based and policy-based methods, using two neural networks: an 'actor' that learns a policy to select actions, and a 'critic' that evaluates those actions by estimating the value function. This architecture enables more stable and efficient learning by providing the actor with feedback on the quality of its actions, reducing variance in policy updates. It is widely used in complex environments where direct policy optimization or pure value estimation alone is insufficient.

Also known as: Actor Critic, Actor-Critic Method, AC, A2C, Advantage Actor-Critic, A3C
🧊Why learn Actor-Critic?

Developers should learn Actor-Critic when working on reinforcement learning projects that require balancing exploration and exploitation in high-dimensional or continuous action spaces, such as robotics, game AI, or autonomous systems. It is particularly useful for tasks where policy gradients (like REINFORCE) suffer from high variance, as the critic's value estimates help reduce this, leading to faster convergence and better performance compared to pure policy-based methods.

Compare Actor-Critic

Actor-Critic vs Deep Q Network→Actor-Critic vs Policy Gradients→Actor-Critic vs Proximal Policy Optimization→

Learning Resources

📄
Spinning Up in Deep RL - Actor-Critic Methods
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Deep Reinforcement Learning: Actor-Critic Algorithms
tutorial
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Coursera - Reinforcement Learning Specialization
course
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YouTube - Actor-Critic Explained
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Reinforcement Learning: An Introduction (Sutton & Barto)
book
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Related Tools

Reinforcement LearningDeep Q NetworkPolicy GradientsProximal Policy OptimizationTemporal Difference Learning

Alternatives to Actor-Critic

Deep Q NetworkPolicy GradientsProximal Policy Optimization