Time and Economic Costs of Reinforcement Learning Environments

The article discusses the concept of a Critic in the context of reinforcement learning. A Critic is a component that evaluates the policy of an agent and provides feedback to improve its performance. The critic uses various features such as memory, game description, trajectory, suggestion, and instruction to evaluate the policy.
The article explains that the critic has several responsibilities, including describing the goal of the game, describing the game environment, and providing suggestions for improving the policy. The critic also evaluates the agent’s actions based on their memory of the previous states and rewards.
The author uses everyday language and engaging metaphors to explain complex concepts, making it easier for readers to understand. For instance, the author compares the critic to a restaurant reviewer who evaluates the quality of food served by a chef. Similarly, the critic evaluates the policy of an agent based on its performance in achieving the goal of the game.
Overall, the summary provides a clear and concise overview of the article’s content, making it accessible to readers who may not be familiar with reinforcement learning or critical components.