Summary of Efficient Recurrent Off-policy Rl Requires a Context-encoder-specific Learning Rate, by Fan-ming Luo et al.
Efficient Recurrent Off-Policy RL Requires a Context-Encoder-Specific Learning Rate
by Fan-Ming Luo, Zuolin Tu, Zefang Huang, Yang Yu
First submitted to arxiv on: 24 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 In this paper, researchers address the challenge of partially observable Markov decision processes (POMDPs) by proposing a novel recurrent off-policy reinforcement learning algorithm called Recurrent Off-policy RL with Context-Encoder-Specific Learning Rate (RESeL). To mitigate partial observability, RESeL uses a context encoder based on recurrent neural networks (RNNs) for unobservable state prediction and a multilayer perceptron (MLP) policy for decision making. By employing a lower learning rate for the context encoder than other MLP layers, RESeL ensures training stability while maintaining efficiency. The algorithm is evaluated in 18 POMDP tasks and five MDP locomotion tasks, demonstrating significant improvements in training stability and notable performance gains over previous recurrent RL baselines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to make decisions when we don’t have all the information. It’s called partially observable Markov decision processes (POMDPs). The researchers developed a special kind of artificial intelligence that can handle this type of situation. They called it Recurrent Off-policy RL with Context-Encoder-Specific Learning Rate (RESeL). This AI uses two parts: one to predict what’s going on and another to make decisions. To make sure it works well, they made the part that predicts what’s going on learn at a slower pace than the decision-making part. They tested RESeL in lots of situations and found that it was really good at making decisions when we didn’t have all the information. |
Keywords
» Artificial intelligence » Encoder » Reinforcement learning
