Summary of Rewarded Region Replay (r3) For Policy Learning with Discrete Action Space, by Bangzheng Li et al.
Rewarded Region Replay (R3) for Policy Learning with Discrete Action Space
by Bangzheng Li, Ningshan Ma, Zifan Wang
First submitted to arxiv on: 26 May 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 This paper introduces Rewarded Region Replay (R3), a novel on-policy algorithm that surpasses Proximal Policy Optimization (PPO) in solving environments with discrete action spaces. R3 employs a replay buffer containing successful trajectories to update the PPO agent using importance sampling, while discarding factors above a certain ratio to reduce variance and stabilize training. In Minigrid environments like DoorKeyEnv and CrossingEnv, R3 outperforms PPO and DDQN (Double Deep Q-Network), a standard off-policy method for discrete actions. Additionally, the Dense R3 algorithm adapts this idea to dense reward settings and surpasses PPO on Cartpole-V1. The code is available at https://github.com/chry-santhemum/R3. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper develops an innovative machine learning technique called Rewarded Region Replay (R3) that improves upon existing methods like Proximal Policy Optimization (PPO). R3 helps computers make better decisions in situations where they have to choose between a few options. The approach uses old, successful actions to teach the computer how to behave better. In tests with different scenarios, R3 performed much better than PPO and another popular method called DDQN. The researchers also applied this technique to a new type of problem and found that it worked well there too. |
Keywords
» Artificial intelligence » Machine learning » Optimization
