Definition
On-policy algorithms require that the agent learns directly from the actions taken by its current policy. This means data collected during exploration is used immediately to update the policy, ensuring consistency but often requiring more samples per update. Examples include REINFORCE and Proximal Policy Optimization (PPO). This contrasts with off-policy methods, which can learn from data generated by different behaviors.
Summary
A reinforcement learning approach where the policy being evaluated and improved is the same as the one used to generate data.
Key Concepts
- reinforcement learning
- policy gradient
- data consistency
- sample efficiency
Use Cases
- Robotics control with safety constraints
- Game playing agents requiring precise updates
- Real-time adaptive systems