Summary of Maximum Entropy On-policy Actor-critic Via Entropy Advantage Estimation, by Jean Seong Bjorn Choe and Jong-kook Kim
Maximum Entropy On-Policy Actor-Critic via Entropy Advantage Estimation
by Jean Seong Bjorn Choe, Jong-Kook Kim
First submitted to arxiv on: 25 Jul 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 A novel approach is proposed for enhancing the performance and stability of policy optimisation in reinforcement learning. The idea is to augment the objective function with an entropy term, which has been shown to be effective in theoretical and empirical studies. However, applying this framework in straightforward on-policy actor-critic settings has remained underexplored due to the difficulty of managing the entropy reward. A simple method is introduced for separating the entropy objective from the original objective, making it possible to implement MaxEnt RL in on-policy settings. The proposed approach is evaluated empirically using Proximal Policy Optimisation (PPO) and Trust Region Policy Optimisation (TRPO), showing improved policy optimisation performance in MuJoCo and Procgen tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper proposes a new way to make artificial intelligence more stable and better at learning. They add a special “noise” term to the AI’s goals, which helps it explore different options and avoid getting stuck in one solution. This is called Maximum Entropy Reinforcement Learning (MaxEnt RL). The problem is that this approach can be tricky to use in everyday situations where the AI has to make decisions based on feedback from the environment. To solve this issue, the researchers developed a simple trick to separate the noise term from the main goal, making it easier to apply MaxEnt RL in real-world scenarios. They tested their method using two different types of tasks and found that it improved the performance of the AI. |
Keywords
* Artificial intelligence * Objective function * Reinforcement learning
