SOTAVerified

Reinforcement Learning (RL)

Reinforcement Learning (RL) involves training an agent to take actions in an environment to maximize a cumulative reward signal. The agent interacts with the environment and learns by receiving feedback in the form of rewards or punishments for its actions. The goal of reinforcement learning is to find the optimal policy or decision-making strategy that maximizes the long-term reward.

Papers

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Showing 526–550 of 15113 papers

TitleStatusHype
Collision Probability Distribution Estimation via Temporal Difference LearningCode1
Reinforcement Learning Pair Trading: A Dynamic Scaling approachCode1
OASIS: Conditional Distribution Shaping for Offline Safe Reinforcement LearningCode1
Learning Goal-Conditioned Representations for Language Reward ModelsCode1
Variable-Agnostic Causal Exploration for Reinforcement LearningCode1
Chip Placement with Diffusion ModelsCode1
Energy-Guided Diffusion Sampling for Offline-to-Online Reinforcement LearningCode1
Reinforcement Learning in High-frequency Market MakingCode1
A Benchmark Environment for Offline Reinforcement Learning in Racing GamesCode1
Transductive Active Learning with Application to Safe Bayesian OptimizationCode1
Can Learned Optimization Make Reinforcement Learning Less Difficult?Code1
Stranger Danger! Identifying and Avoiding Unpredictable Pedestrians in RL-based Social Robot NavigationCode1
Hindsight Preference Learning for Offline Preference-based Reinforcement LearningCode1
RobocupGym: A challenging continuous control benchmark in RobocupCode1
PUZZLES: A Benchmark for Neural Algorithmic ReasoningCode1
Memory-Enhanced Neural Solvers for Efficient Adaptation in Combinatorial OptimizationCode1
Soft-QMIX: Integrating Maximum Entropy For Monotonic Value Function FactorizationCode1
RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-FoldCode1
Discovering Minimal Reinforcement Learning EnvironmentsCode1
Investigating Pre-Training Objectives for Generalization in Vision-Based Reinforcement LearningCode1
ICU-Sepsis: A Benchmark MDP Built from Real Medical DataCode1
HackAtari: Atari Learning Environments for Robust and Continual Reinforcement LearningCode1
Strategically Conservative Q-LearningCode1
Fine-Grained Causal Dynamics Learning with Quantization for Improving Robustness in Reinforcement LearningCode1
CommonPower: A Framework for Safe Data-Driven Smart Grid ControlCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1PPGMean Normalized Performance0.76—Unverified
2PPOMean Normalized Performance0.58—Unverified