Learning Safe Agent Behaviour from Human Preferences and Justifications via World Models
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arXiv:2607.13172v1 Announce Type: new Abstract: We address the problem of safely training an agent policy and deploying a good and safe policy, in settings where the environment dynamics are unknown and no suitable reward function is available. In the context of safety-critical environments, we consider traditional reinforcement learning impractical and resort to the resource of human input. We introduce DROPJ, a human-centred method for both safe training and deployment. We first learn a world…
1Key Takeaways
- arXiv:2607.13172v1 Announce Type: new Abstract: We address the problem of safely training an agent policy and deploying a good and safe policy, in settings where the environment dynamics are unknown and no suitable reward function is available.
- In the context of safety-critical environments, we consider traditional reinforcement learning impractical and resort to the resource of human input.
- We introduce DROPJ, a human-centred method for both safe training and deployment.
2AIWedia Score
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3Why it matters
Research breakthroughs often arrive in products months later—early signals matter for strategy. arXiv cs.AI reports that arXiv:2607.13172v1 Announce Type: new Abstract: We address the problem of safely training an agent policy and deploying a good and safe policy, in settings where the environment dynamics are unknown and no suitable reward function is available.
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