Value-based and policy-based methods are two fundamental approaches in reinforcement learning, differing primarily in how they guide an agent’s decision-making. Value-based methods focus on learning the value of states or actions (e.g., how rewarding a specific action in a state might be), while policy-based methods directly learn the policy (e.g., the strategy mapping states to actions) without relying on explicit value estimates. The choice between them depends on factors like problem complexity, action space, and desired stability.
Value-based methods, such as Q-learning, work by building a value function (like a Q-table) that estimates the expected long-term reward for taking an action in a state. The agent then selects actions that maximize this value. For example, in a grid-world game, the agent might learn that moving right in a specific cell yields a higher Q-value than moving left. These methods excel in environments with discrete, manageable action spaces but struggle in continuous or high-dimensional settings. A limitation is that deriving a policy from values (e.g., always picking the highest Q-value) can become inefficient when the action space grows, as maintaining accurate value estimates for every state-action pair becomes computationally expensive.
Policy-based methods, like REINFORCE or policy gradient algorithms, bypass value estimation by directly optimizing a parameterized policy. Instead of tracking values, the policy (e.g., a neural network) is trained to output probabilities for each action, which are adjusted to maximize rewards. For instance, a robot arm control task with continuous joint movements might use a policy network to sample torque values directly from a probability distribution. This approach handles continuous actions and complex environments more naturally but tends to require more samples to converge due to higher variance in gradient estimates. Hybrid methods like Actor-Critic combine both approaches: a policy (actor) decides actions, while a value function (critic) evaluates those actions, reducing variance and improving stability. This balance makes hybrids popular in modern applications like game AI or robotics.