Summary of Long-term Safe Reinforcement Learning with Binary Feedback, by Akifumi Wachi et al.
Long-term Safe Reinforcement Learning with Binary Feedback
by Akifumi Wachi, Wataru Hashimoto, Kazumune Hashimoto
First submitted to arxiv on: 8 Jan 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 The proposed Long-term Binaryfeedback Safe RL (LoBiSaRL) algorithm addresses the limitations of existing safe reinforcement learning (RL) methods by guaranteeing safety during the learning process, even with unknown and stochastic state transition functions. LoBiSaRL optimizes a policy to maximize rewards while ensuring that the agent only executes safe state-action pairs throughout each episode with high probability. The algorithm models the binary safety function using a generalized linear model (GLM) and conservatively takes only safe actions at every time step, inferring their effect on future safety under proper assumptions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary LoBiSaRL is an algorithm that helps robots and machines learn new tasks without making mistakes or causing harm. It’s like training a child to play safely without getting hurt. The algorithm uses special math called GLM to figure out what actions are safe and which ones might cause problems. Then, it only chooses the safe actions, so the machine doesn’t make any bad decisions. |
Keywords
* Artificial intelligence * Probability * Reinforcement learning
