Risk-Aware Active Inverse Reinforcement Learning
📖 Description
Risk-Aware Active Inverse Reinforcement Learning Daniel S. Brown, Yuchen Cui, and Scott Niekum Department of Computer Science University of Texas at Austin, United States [email address redacted], [email address redacted], [email address redacted] Abstract: Active learning from demonstration allows a robot to query a human for speci?c types of input to achieve ef?cient learning. Existing work has explored a variety of active query strategies; however, to our knowledge, none of these strate- gies directly minimize the performance risk of the policy the robot is learning. Utilizing recent advances in performance bounds for inverse reinforcement learn- ing, we propose a risk-aware active inverse reinforcement learning algorithm that focuses active queries on areas of the state space with the potential for large gen- eralization error. We show that risk-aware active learning outperforms standard active IRL approaches on gridworld, simulated driving, and table setting tasks, while also providing...
📊 Game Impacts
Not verified in gameWhich variables this event was proposed to move, and in which direction. The magnitudes are held in the corpus but are not shown here, because they have not been verified against the shipped game. They come from pdoom-data. They describe what an event was proposed to do, not what the shipped game does with it. Most events in the corpus are flavour: they are shown for colour and do not move any game variable. Only a small minority reach the systems below, and several of the variables listed here are not read by the game at all yet. Treat this table as a design proposal under review, not as a measurement of play. Corrections and arguments are welcome — the suggestion links at the foot of this page go straight to the data repo.
| Variable | Direction | Condition |
|---|---|---|
| Research | proposed: up | Always |
| Papers | proposed: up | Always |
| Vibey Doom | proposed: up | Always |
💭 Reactions
"Advances our understanding of AI safety"
"Published in academic venue"
🔗 Sources
🤝 Found an Issue?
This event data is sourced from the pdoom-data repository. If you notice errors or want to suggest improvements:
GitHub Issue (Preferred) 📧 Email (No GitHub)