AI & ML
Quantifying generalization in reinforcement learning
OpenAI Blog0 views
We’re releasing CoinRun, a training environment which provides a metric for an agent’s ability to transfer its experience to novel situations and has already helped clarify a longstanding puzzle in reinforcement learning. CoinRun strikes a desirable balance in complexity: the environment is simpler than traditional platformer games like Sonic the Hedgehog but still poses a worthy generalization challenge for state of the art algorithms.
Source：https://openai.com/index/quantifying-generalization-in-reinforcement-learningRelated posts
Sharp Non-Asymptotic Analysis of the Penalized Challenger in $\beta$-EB-TCI for Bernoulli Bandits
0arXiv stat.ML
Toward Optimal Switching Regret for Multi-Armed Bandits with Oblivious Adversary
0arXiv stat.ML
Spectral Condition for $\mu$P under Width-Depth Scaling
0arXiv stat.ML
A Function-Space Stability Boundary for Generalization in Interpolating Learning Systems
0arXiv stat.ML
Who Gets Watched? Allocating Scarce Escalation Resources on Pediatric Wards with Neural Index Policies
0arXiv stat.ML
Minimax Alternating Regret for the Experts Problem and Online Convex Optimization
0arXiv stat.ML