What are the limitations of PPO in reinforcement learning?

What are the limitations of PPO in reinforcement learning?

PPO, or Proximal Policy Optimization, has several limitations, including sensitivity to hyperparameters, sample inefficiency, and difficulty in handling high-dimensional action spaces.

Can you explain why hyperparameter sensitivity is a problem?

Hyperparameter sensitivity means that small changes in parameters can lead to significantly different performance, making it hard to find the optimal settings.

What do you mean by sample inefficiency?

Sample inefficiency refers to the amount of data required to train the model effectively. PPO often needs a large number of samples to converge.

Are there any alternatives to PPO that address these limitations?

Yes, alternatives like TRPO (Trust Region Policy Optimization) and DDPG (Deep Deterministic Policy Gradient) can address some of these limitations.

How does PPO compare to TRPO in terms of performance?

PPO is generally easier to implement and tune than TRPO, but TRPO can provide more stable performance in certain environments.