Temporal Difference, SARSA, and Q-learning in Reinforcement Learning
What does temporal difference mean in this context?
How is SARSA different from Q-learning?
Temporal Difference (TD)
Temporal difference, often abbreviated as TD, refers to a learning technique used in reinforcement learning. It is a prediction-based learning method that updates the value of states or state-action pairs based on the discrepancy, or "temporal difference," between predicted future rewards and observed rewards. TD learning is a key component of several reinforcement learning algorithms, including SARSA and Q-learning.
SARSA
SARSA is an on-policy reinforcement learning algorithm used for estimating the optimal action-value function, known as the Q-function. The name SARSA stands for "State-Action-Reward-State-Action," which describes the key components of its learning process. In SARSA, the agent interacts with the environment, observes the current state, takes an action, receives a reward, observes the next state, and then selects the next action based on its policy. SARSA updates the Q-values based on the transitions it experiences and follows the current policy for action selection. It is an example of an on-policy method because it learns the value of the policy that it is currently following.
Q-learning
Q-learning is an off-policy reinforcement learning algorithm used for estimating the optimal action-value function (Q-function). Unlike SARSA, Q-learning does not necessarily follow the current policy during action selection; instead, it explores and learns the optimal policy by selecting actions that maximize the estimated Q-values. Q-learning updates the Q-values based on the maximum expected future reward for each state-action pair and uses an exploration strategy (often epsilon-greedy) to balance exploration and exploitation. Over time, Q-learning converges to the optimal policy and Q-values. It is an example of an off-policy method because it learns the value of the optimal policy independently of the policy it follows during exploration.
Temporal difference learning plays a crucial role in reinforcement learning by helping agents to update their value estimates based on the temporal discrepancy between predicted and observed rewards. SARSA and Q-learning are two popular reinforcement learning algorithms that utilize temporal difference learning in their optimization processes.
Differences between SARSA and Q-learning
SARSA and Q-learning differ primarily in their approach to action selection and the policy they follow during learning. SARSA follows the current policy and updates its Q-values accordingly, while Q-learning explores and learns the optimal policy independently. This distinction makes SARSA an on-policy method and Q-learning an off-policy method.
In SARSA, the agent learns the value of the policy it is currently following, making it more conservative in its exploration of the environment. On the other hand, Q-learning aims to find the optimal policy by maximizing the Q-values without strictly adhering to the current policy. This allows Q-learning to potentially find better policies but also introduces more exploration in the learning process.
Both SARSA and Q-learning have their strengths and weaknesses, making them suitable for different tasks and environments in reinforcement learning. Understanding the nuances of on-policy and off-policy methods, along with the role of temporal difference learning, is essential for designing effective reinforcement learning algorithms.