Policy
In reinforcement learning (RL), a policy is the strategy that an agent follows to decide which action to take given a particular state. It defines the mapping from states to actions with the goal of maximizing cumulative rewards.
Background
A policy can be:
- Deterministic: a fixed action is chosen for each state.
- Stochastic: a probability distribution over possible actions is used.
Policies are central to RL because they represent the agent’s learned behavior. The ultimate objective of training is to find an optimal policy.
Examples
- Gaming: an RL agent learning to play chess or Go by improving its policy.
- Robotics: policies guiding a robotic arm to manipulate objects.
- Resource management: dynamic allocation of servers in cloud computing.
Strengths and challenges
- ✅ Captures the agent’s decision-making logic.
- ✅ Can adapt to uncertain or dynamic environments.
- ❌ Hard to compute optimal policies in large state-action spaces.
- ❌ Requires extensive exploration and training.
In practice, policies are rarely stored as explicit lookup tables, since real-world environments often have vast or continuous state spaces. Instead, policies are often parameterized functions, frequently implemented with deep neural networks. This approach, known as policy-based reinforcement learning, focuses on directly optimizing the policy parameters to maximize expected cumulative rewards.
Another key distinction lies between fixed policies and adaptive policies. A fixed policy might correspond to a rule-based system or a hard-coded strategy, while adaptive policies evolve through trial and error as the agent interacts with its environment, gradually refining its decision-making process.
In safety-critical domains like healthcare or autonomous driving, it is not enough for a policy to be mathematically optimal—it must also be interpretable and trustworthy. This has fueled growing research into explainable policies, aiming to ensure that human operators can understand, validate, and regulate the agent’s behavior.
📚 Further Reading
- Sutton, R. S., & Barto, A. G. (2018). Reinforcement Learning: An Introduction.
