Markov Models
COOM: A Game Benchmark for Continual Reinforcement Learning Tristan Tomilin 1 Meng Fang
RL benchmark tailored for embodied pixel-based RL.
When Y our AIs Deceive You: Challenges of Partial Observability in Reinforcement Learning from Human Feedback Leon Lang University of Amsterdam Davis Foote
Modeling the human as Boltzmann-rational w.r.t. a belief over trajectories, we prove conditions under which RLHF is
Double Pessimism is Provably Efficient for Distributionally Robust Offline Reinforcement Learning: Generic Algorithm and Robust Partial Coverage
Typically, deep RL algorithms learn a policy in an online trial-and-error fashion using millions to billions of data.
QGym: Scalable Simulation and Benchmarking of Queuing Network Controllers
Queuing network control determines the allocation of scarce resources to manage congestion, a fundamental problem in manufacturing, communications, and healthcare.
Seeing is not Believing: Robust Reinforcement Learning against Spurious Correlation
Please refer to the website for more details.
ODE-based Recurrent Model-free Reinforcement Learning for POMDPs
In partially observable (PO) environments, how to infer unseen information from raw observations puzzled the agents.
ProteinGym: Large-Scale Benchmarks for Protein Fitness Prediction and Design
Addressing these challenges requires scale. To that end we introduce ProteinGym, a large-scale and holistic set of benchmarks specifically designed for protein fitness prediction and design.