Summary of Simplifying Deep Temporal Difference Learning, by Matteo Gallici et al.
Simplifying Deep Temporal Difference Learning
by Matteo Gallici, Mattie Fellows, Benjamin Ellis, Bartomeu Pou, Ivan Masmitja, Jakob Nicolaus Foerster, Mario Martin
First submitted to arxiv on: 5 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 The abstract discusses the limitations of traditional reinforcement learning (RL) techniques like Q-learning and TD algorithms with off-policy data. These methods require additional tricks to stabilize training, such as large replay buffers and target networks. The paper investigates whether it’s possible to accelerate and simplify off-policy TD training while maintaining stability. The authors demonstrate that regularization techniques can yield provably convergent TD algorithms without the need for a target network or replay buffer. Empirical results show that online, parallelized sampling enabled by vectorized environments stabilizes training. The proposed PQN algorithm is competitive with complex methods like Rainbow and PPO-RNN, and up to 50x faster than traditional DQN. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Reinforcement learning (RL) helps computers learn from trial and error. But some RL algorithms need extra help to work well, like a big memory buffer or multiple neural networks. Researchers are looking for ways to make these algorithms better and more efficient. This paper shows that adding special techniques can make TD algorithms stable without needing all those extras. The authors also test an algorithm called PQN, which is surprisingly fast and competitive with other good RL methods. |
Keywords
» Artificial intelligence » Regularization » Reinforcement learning » Rnn
