Reinforcement Learning: SARSA and Q-Learning
Similarities and Differences between SARSA and Q-Learning
Reinforcement Learning aims for an agent to find optimal actions in an environment that maximizes its long-term reward by continually interacting with its environment. Interaction of the agent with its environment changes the environment’s state, and as a result, the agent receives a numerical reward from the environment. The optimal actions that the Agent takes are referred to as a policy.