What are policy-based methods in reinforcement learning?

Policy-based methods in reinforcement learning (RL) are algorithms that directly learn a policy—a strategy for choosing actions—without relying on intermediate value functions. Unlike value-based methods, which estimate the expected rewards of states or actions and derive policies from those estimates, policy-based approaches optimize the policy itself. These methods typically parameterize the policy using a function (like a neural network) and adjust its parameters to maximize the expected cumulative reward. For example, if an agent learns to play a game, the policy might map game states to probabilities of taking specific actions, which are updated over time to favor actions leading to higher rewards.

A key advantage of policy-based methods is their ability to handle continuous action spaces and stochastic policies. For instance, in robotics, where actions might involve precise motor control (like torque values), policy-based methods can output continuous actions directly. Algorithms like REINFORCE or Proximal Policy Optimization (PPO) are examples. REINFORCE uses gradient ascent to adjust policy parameters based on the rewards from sampled trajectories, while PPO introduces constraints to ensure policy updates stay within a safe range, improving stability. These methods often employ exploration strategies by default, as the policy’s stochastic nature encourages trying diverse actions, which is useful in environments where the optimal strategy isn’t obvious.

However, policy-based methods can suffer from high variance in updates, leading to slower convergence. Techniques like entropy regularization (to encourage exploration) or advantage estimation (to reduce variance) are often used to address this. They are particularly useful in complex environments where precise value estimation is difficult, such as training robotic arms or playing games with high-dimensional state spaces. Compared to value-based methods like Q-learning, policy-based approaches trade off sample efficiency for flexibility in action selection, making them a popular choice for real-world applications requiring nuanced control.