What is Actor-Critic Methods
A class of reinforcement learning methods where one component selects actions and another evaluates their utility.
Definition
There are two roles in actor-critic methods. The actor proposes an action in the environment, and the critic evaluates the expected benefit and helps the actor learn faster. This approach is often used where the agent must make sequential decisions: in robotics, games, resource management or route optimization.
Example
The robot learns to walk: the actor chooses the movement of the leg, and the critic evaluates whether it helped maintain balance and get closer to the goal.
Why it matters
The term is important for understanding autonomous agents and systems that do not just classify data, but learn to act in a changing environment.
How it works
The agent receives the state of the environment, selects an action, receives a reward, and updates two parts of the model: the action strategy and the evaluation function.
Where it is used
- reinforcement learning
- robotics
- autonomous agents and games
Limitations
Methods can be unstable, require many simulations, and are sensitive to rewards. An incorrectly specified reward leads to unexpected behavior by the agent.