Gradient ascent is the counterpart to gradient descent — it is used to maximize a function by moving in the direction of the positive gradient. In reinforcement learning, this is particularly relevant for policy gradient methods, where the goal is to directly optimize a policy by increasing the expected return.
The idea is simple: compute the gradient of the expected reward with respect to the policy parameters and adjust those parameters to increase the reward. Over time, the policy becomes better at choosing actions that lead to high rewards. Like gradient descent, gradient ascent depends on a learning rate and can suffer from issues like local maxima or slow convergence.
One of the most well-known algorithms that uses gradient ascent is REINFORCE, which updates policy parameters based on the reward received and the likelihood of actions taken. More advanced techniques, like Actor-Critic or PPO, also build on this idea but introduce variance reduction techniques and stability improvements. Gradient ascent is essential for learning in environments where the objective is not predefined, but must be discovered through interaction.