Q-learning is a model-free reinforcement learning algorithm used to find the optimal action-selection policy for any given Markov decision process (MDP). The goal of Q-learning is to learn a policy, which tells an agent what action to take under what circumstances, by learning the Q-values for each state-action pair. The Q-value represents the expected cumulative reward an agent will receive starting from a particular state and taking a particular action, and then following the optimal policy thereafter.
The algorithm works by iteratively updating the Q-values based on the Bellman equation, which states that the optimal Q-value for a state-action pair is equal to the immediate reward obtained from taking that action in that state, plus the discounted maximum future reward that can be obtained from the next state, assuming the agent follows the optimal policy.
The update rule for Q-learning is as follows:
\[Q(s, a) \leftarrow Q(s, a) + \alpha [r + \gamma \max_{a'} Q(s', a') - Q(s, a)]\]
where:
- \(Q(s, a)\) is the Q-value for state \(s\) and action \(a\).
- \(r\) is the immediate reward obtained from taking action \(a\) in state \(s\).
- \(\alpha\) is the learning rate, which controls how much the Q-values are updated on each iteration.
- \(\gamma\) is the discount factor, which determines the importance of future rewards.
- \(s'\) is the next state.
- \(a'\) is the next action.
Q-learning is an off-policy algorithm, meaning that it learns the optimal policy while following a different policy (typically an ε-greedy policy) to explore the state-action space. This allows Q-learning to balance exploration and exploitation, ultimately converging to the optimal policy.
Q-learning has been successfully applied to various problems, including game playing, robotic control, and optimization.
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