Summary of Frugal Actor-critic: Sample Efficient Off-policy Deep Reinforcement Learning Using Unique Experiences, by Nikhil Kumar Singh and Indranil Saha
Frugal Actor-Critic: Sample Efficient Off-Policy Deep Reinforcement Learning Using Unique Experiences
by Nikhil Kumar Singh, Indranil Saha
First submitted to arxiv on: 5 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Robotics (cs.RO); Systems and Control (eess.SY)
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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 proposes a method to improve sample efficiency in off-policy actor-critic reinforcement learning (RL) algorithms, which are used to synthesize control policies for complex dynamical systems. The key innovation is selecting unique samples from the replay buffer during exploration, reducing its size while maintaining the independent and identically distributed (IID) nature of the samples. The method involves identifying important state variables, partitioning the state space into abstract states, and then selecting experiences with distinct state-reward combinations using a kernel density estimator. Compared to vanilla off-policy actor-critic algorithms, this approach is shown to converge faster and achieve better reward accumulation on various continuous control benchmarks in the Gym environment. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us learn how to make computers smarter by improving their ability to teach themselves new skills. It’s like teaching a robot to do new things without needing human help every time. The researchers found a way to make the computer learn more efficiently, which means it can get better at doing tasks on its own faster. They did this by picking out special experiences that are useful for learning and adding them to a memory bank called the replay buffer. This makes the computer smarter and allows it to do tasks without needing human help. |
Keywords
* Artificial intelligence * Reinforcement learning
