> ML_ALGORITHM // PROXIMAL-POLICY-OPTIMIZATION-PPO_v1.0
Proximal Policy Optimization (PPO)
The industry-standard on-policy reinforcement learning algorithm providing stable, robust policy optimization via clipped surrogate objective functions.
On-Policy Policy Gradient RLreinforcement-learningblack-boxlarge (>100k)
Back to All AlgorithmsComputational Complexity
Training Complexity:O(epochs * batch_size * forward_backward)
Inference Complexity:O(policy_forward)
Hardware Profile
CPU Friendly:No
Requires GPU:Yes
Memory Footprint:high
Interpretability & Data
Interpretability Tier:black-box
Training Data Needs:large (>100k)
Interpretability Assessment
Clipping objective prevents catastrophic policy destruction without requiring expensive second-order Hessian calculations.
Suitable Tasks & Supported Modalities
Suitable Tasks:
reinforcement learningllm alignment rlhfrobotics simulation
Supported Modalities:
tabulartextimage
Implementing Libraries
Foundational Literature
Proximal Policy Optimization Algorithms (PPO)John Schulman, Filip Wolski (2017) · arXiv preprint
Common Pitfalls & Warnings
- Reward hacking when fine-tuning LLMs with RLHF if KL penalty against reference model is tuned too loose
- High sample inefficiency requiring millions of simulated environment interactions