Policy-based and value-based reinforcement learning (RL) methods are two primary categories of algorithms used in RL. While both use the same fundamental approach of learning through an agents interaction with its environment, their approaches to decision-making differ significantly.
Value-based RL methods aim to learn the value of different states or actions by estimating the expected cumulative reward that an agent could receive from the current state. The agent then chooses the action that leads to the highest expected reward. Value-based methods typically use a Q-function to compute the value of different state-action pairs. Q-learning is a well-known example of a value-based RL method. In Q-learning, the agent updates its Q-table to learn the optimal action for each state using a formula based on the Bellman equation.
On the other hand, policy-based RL methods directly learn the optimal policy by mapping states to actions without explicitly computing the value function. Policy-based methods can work with continuous action spaces and complex policies that value-based methods struggle with. These methods learn the optimal policy through optimization, using a function approximator such as a neural network. The policy can be represented as Gaussian distributions in continuous action spaces, and the parameters for the distributions can be optimized using gradient descent. An example of a policy-based approach is the REINFORCE algorithm.
To summarize, value-based RL methods find the optimal policy indirectly by determining the optimal value function. Policy-based RL methods, on the other hand, directly learn the optimal policy. Value-based methods work well when the agent has access to a model of the environment, whereas policy-based methods can work in model-free environments.