Standard 8 stops to get here · leads to 2

Policy Gradient

RL methods that directly optimize the policy by computing gradients of expected reward with respect to policy parameters.

Your route here

8 stops · basics first
  1. Machine Learning ✓ understood

    Building systems that learn patterns from data instead of following hand-written rules, getting better at a task as they see more examples.

  2. Reinforcement Learning ✓ understood

    Learning through interaction with an environment, receiving rewards or penalties to learn optimal behavior policies.

  3. Agent ✓ understood

    In RL, the learner or decision-maker that takes actions in an environment to maximize cumulative reward.

  4. Policy ✓ understood

    A strategy or mapping from states to actions that defines the agent's behavior in reinforcement learning.

  5. Dataset ✓ understood

    A collection of data examples used for training, validating, or testing machine learning models.

  6. Training ✓ understood

    The process of fitting a model to data by repeatedly measuring how wrong its outputs are and adjusting its parameters to reduce that error.

  7. Loss Function ✓ understood

    A function that scores how wrong a model's prediction is as a single number, which training then works to make as small as possible.

  8. Gradient Descent ✓ understood

    An optimization method that repeatedly moves a model's parameters a small step in the direction that most reduces the loss.

  9. Policy Gradient · you are here ✓ understood

Where it sits

Before this

Policy Gradient Descent
Policy Gradient

Leads to

Actor-Critic PPO

Explore nearby

Agents & RL Actor-Critic RL architecture with two components: an actor (policy) that selects actions and a critic (value function) that evaluates them. Agents & RL PPO Proximal Policy Optimization - a stable and efficient policy gradient algorithm widely used in RLHF for training LLMs. Agents & RL Q-Learning A model-free RL algorithm that learns action-value functions (Q-values) to determine optimal actions in each state. Language & LLMs RLHF Reinforcement Learning from Human Feedback - training models using human preferences to align behavior with human values. Agents & RL Model-Free RL Reinforcement learning directly learning policies or value functions without modeling environment dynamics.

In the research

All papers →

3 papers that build on Policy Gradient .