Q-Learning
Q-Learning is a model-free reinforcement learning algorithm. It allows an agent to learn an optimal policy by maximizing cumulative rewards without prior knowledge of the environment. The algorithm learns a Q-value function, which estimates the expected reward of taking a given action in a given state.
Background
Proposed by Christopher Watkins in 1989, Q-Learning has become one of the foundational methods in reinforcement learning. It inspired numerous modern approaches, such as Deep Q-Networks (DQN), which combine Q-Learning with deep neural networks to scale to high-dimensional problems.
Applications
- Gaming: used in early reinforcement learning systems and as a precursor to AlphaGo.
- Robotics: teaching robots to explore environments and avoid obstacles.
- Finance: sequential decision-making for trading and portfolio optimization.
- Resource management: traffic signal control, energy grid optimization.
Strengths and weaknesses
- ✅ Does not require an explicit model of the environment.
- ✅ Proven convergence to the optimal policy under exploration conditions.
- ❌ Struggles with large state–action spaces without approximation.
- ❌ Training can be slow and unstable without enhancements (e.g., replay buffers, neural networks).
At its core, Q-Learning works by iteratively updating estimates of the quality (Q-value) of state–action pairs. After each interaction with the environment, the agent refines its table of Q-values according to the reward received and the maximum expected future reward. Over time, this iterative bootstrapping process converges toward the optimal action-value function, provided that the agent explores sufficiently.
The algorithm’s elegance lies in its simplicity: it does not require knowledge of transition probabilities or reward distributions, unlike model-based methods. However, when the number of states or actions grows very large—as in complex games or continuous control—classic Q-Learning quickly becomes impractical, leading to the rise of function approximation techniques such as Deep Q-Networks (DQN).
Another important concept is the exploration–exploitation trade-off. Q-Learning agents must balance trying new actions to discover rewards (exploration) with sticking to the best-known actions (exploitation). Strategies like ε-greedy policies or more sophisticated methods such as Boltzmann exploration are used to manage this balance.
📚 Further Reading
- Watkins, C. (1989). Learning from Delayed Rewards.
- Sutton & Barto (2018). Reinforcement Learning: An Introduction.
