Proximal Policy Optimization (PPO) Simulation

This simulation demonstrates how an agent learns to navigate to a goal using Proximal Policy Optimization (PPO). PPO is an on-policy reinforcement learning algorithm that uses a "clipping" mechanism to prevent large policy updates, making training more stable and efficient.

Environment

The agent (blue) must navigate to the goal (green) while avoiding obstacles (red).

Total Reward: 0

PPO Parameters

0.2
0.1
4

Policy Visualization

This shows the current policy of the agent (arrows indicate preferred actions in each state).

What is PPO?

Proximal Policy Optimization (PPO) is a policy gradient method for reinforcement learning developed by OpenAI in 2017. It has become one of the most popular RL algorithms due to its simplicity and effectiveness.

PPO aims to balance two objectives:

  • Improving the agent's policy to maximize rewards
  • Preventing large policy updates that could destabilize training

Key Innovations in PPO

The central innovation in PPO is the clipped surrogate objective function:

LCLIP(θ) = E[min(rt(θ)At, clip(rt(θ), 1-ε, 1+ε)At)]

where:

  • rt(θ) is the ratio of probabilities under new and old policies
  • At is the advantage estimate
  • ε is the clipping parameter (usually 0.1 or 0.2)

The clipping mechanism ensures that the policy update stays within a "trust region" by limiting how much the new policy can deviate from the old one.

How PPO Works in This Simulation

  1. The agent collects experience by interacting with the environment using its current policy
  2. Advantages are computed for each state-action pair
  3. The policy is updated using the clipped surrogate objective
  4. Multiple optimization epochs are performed on the same batch of data
  5. The process repeats with the new policy

You can observe these steps in action in the simulation tab by watching the policy visualization and training metrics.

PPO vs. Other RL Algorithms

PPO improves upon earlier algorithms in several ways:

  • vs. REINFORCE: More stable training due to advantage estimation and clipping
  • vs. TRPO: Simpler implementation while maintaining similar performance
  • vs. A2C/A3C: Better sample efficiency and more stable policy updates
  • vs. Off-policy algorithms (DQN, DDPG): Less sensitive to hyperparameters and often more stable

Training Progress

Episodes: 0 / 100

Reward Over Time

Policy Loss

Value Loss