A typical reinforcement learning system that can learn how to play Atari games consists of the following components:

1. Environment: The Atari games environment in which the agent interacts and learns.

2. Agent: The decision-making entity that interacts with the environment and takes actions to maximize its rewards.

3. Policy: A function that maps state to actions. The policy determines the agent's behavior in the environment.

4. Value Function: A function that estimates the expected cumulative reward of a state or state-action pair. The value function is used to evaluate the quality of the agent's policy.

5. Experience Buffer: A buffer that stores the agent's experiences (i.e., state, action, reward, next state) for training the agent's policy and value function.

6. Exploration Strategy: A mechanism for the agent to explore the environment to learn about the state-action space.

7. Training Algorithm: An algorithm that uses the agent's experiences stored in the experience buffer to update the agent's policy and value function.

8. Neural Network: A deep neural network that is used to represent the policy and/or the value function. The neural network is trained by the training algorithm to approximate the optimal policy and/or value function.

9. Evaluation Metrics: Metrics used to evaluate the performance of the agent, such as the average score or win rate on the Atari games.
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