Summary of Accelerating Proximal Policy Optimization Learning Using Task Prediction For Solving Environments with Delayed Rewards, by Ahmad Ahmad et al.
Accelerating Proximal Policy Optimization Learning Using Task Prediction for Solving Environments with Delayed Rewards
by Ahmad Ahmad, Mehdi Kermanshah, Kevin Leahy, Zachary Serlin, Ho Chit Siu, Makai Mann, Cristian-Ioan Vasile, Roberto Tron, Calin Belta
First submitted to arxiv on: 26 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper addresses the issue of delayed rewards in reinforcement learning (RL), which can impact the performance of Proximal Policy Optimization (PPO) models. The authors introduce a hybrid policy architecture that combines an offline policy trained on expert demonstrations with an online PPO policy, as well as a reward shaping mechanism using Time Window Temporal Logic (TWTL). The hybrid approach leverages offline data throughout training while maintaining PPO’s theoretical guarantees, ensuring monotonic improvement over previous iterations. Additionally, the authors prove that their approach preserves the optimal policy of the original problem and demonstrate its effectiveness through experiments on inverted pendulum and lunar lander environments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps robots learn new tasks faster and better by fixing a problem called delayed rewards in reinforcement learning. The authors use two new ideas to make this work: combining old knowledge with new learning, and adjusting rewards based on time and goals. They test these ideas on simple simulations of balancing an inverted pendulum and landing a spacecraft, showing that their approach is better than others at both getting the job done quickly and doing it correctly. |
Keywords
* Artificial intelligence * Optimization * Reinforcement learning
