🔬

EnsembleDAgger: A Bayesian Approach to Safe Imitation Learning

📅 2018
technical research breakthrough
🔵 Rare

📖 Description

EnsembleDAgger: A Bayesian Approach to Safe Imitation Learning Kunal Menda,1Katherine Driggs-Campbell,2and Mykel J. Kochenderfer1 Abstract ? Although imitation learning is often used in robotics, the approach frequently suffers from data mismatch and compounding errors. DAgger is an iterative algorithm that addresses these issues by aggregating training data from both the expert and novice policies, but does not consider the impact of safety. We present a probabilistic extension to DAgger, which attempts to quantify the con?dence of the novice policy as a proxy for safety. Our method, EnsembleDAgger, approximates a Gaussian Process using an ensemble of neural networks. Using the variance as a measure of con?dence, we compute a decision rule that captures how much we doubt the novice, thus determining when it is safe to allow the novice to act. With this approach, we aim to maximize the novice?s share of actions, while constraining the probability of failure. We demonstrate improved ...

📊 Game Impacts

Not verified in game

Which 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

🔬 Safety Researcher Reaction: ⚠️ Placeholder - Needs Real Quote
"Notable work on AI safety"
📰 Media Reaction: ⚠️ Placeholder - Needs Real Quote
"Published in academic venue"
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🔗 Sources

🏷️ Event Metadata

Think this event's metadata could be improved? Category and tags describe the real-world event and are maintained in pdoom-data. Rarity, game impacts and p(doom) effects are game-mechanical values owned by pdoom1. Each link below goes to the repository that decides that field.

📁 Category Technical Research Breakthrough → Suggest different category
⭐ Rarity 🔵 Rare → Suggest different rarity
🏷️ Tags (0) → Suggest tag changes
📊 Game Impacts (3) 3 variable changes → Suggest impact changes
☢️ p(Doom) Impact No direct impact → Suggest p(doom) change
📝 General Metadata Year, description, reactions → Comprehensive review

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