Summary of Maxinforl: Boosting Exploration in Reinforcement Learning Through Information Gain Maximization, by Bhavya Sukhija et al.
MaxInfoRL: Boosting exploration in reinforcement learning through information gain maximization
by Bhavya Sukhija, Stelian Coros, Andreas Krause, Pieter Abbeel, Carmelo Sferrazza
First submitted to arxiv on: 16 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Robotics (cs.RO)
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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 MaxInfoRL, a framework for balancing intrinsic and extrinsic exploration in reinforcement learning (RL) algorithms. The traditional approach to RL involves undirected exploration, selecting random sequences of actions. However, directed exploration using intrinsic rewards like curiosity or model epistemic uncertainty can also be effective. The challenge lies in effectively balancing task and intrinsic rewards, which often depends on the specific task. MaxInfoRL addresses this challenge by steering exploration towards informative transitions, maximizing intrinsic rewards like information gain about the underlying task. When combined with Boltzmann exploration, this approach naturally trades off maximization of the value function with that of the entropy over states, rewards, and actions. The paper demonstrates the effectiveness of this approach in multi-armed bandits and various off-policy model-free RL methods for continuous state-action spaces. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Reinforcement learning is a way that computers learn to make good choices by trying out different options. Usually, they choose random options to see what works best. But sometimes it’s better to try new things to find even better ways. The problem is figuring out when to do each one. This paper shows how to make a computer balance trying the best option now with exploring new possibilities that might be even better later. It does this by using rewards that tell the computer what’s good and what’s not. By combining these rewards, the computer can decide when to stick with what it knows and when to try something new. |
Keywords
» Artificial intelligence » Reinforcement learning
