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

Showing 15011525 of 15113 papers

TitleStatusHype
Combining Reinforcement Learning and Constraint Programming for Combinatorial OptimizationCode1
KuaiSim: A Comprehensive Simulator for Recommender SystemsCode1
Actor Prioritized Experience ReplayCode1
Langevin Soft Actor-Critic: Efficient Exploration through Uncertainty-Driven Critic LearningCode1
Combining Deep Reinforcement Learning and Search for Imperfect-Information GamesCode1
Language Models are Few-Shot ButlersCode1
Large Batch Experience ReplayCode1
Large Batch Simulation for Deep Reinforcement LearningCode1
A Cooperative Multi-Agent Reinforcement Learning Framework for Resource Balancing in Complex Logistics NetworkCode1
Large Neighborhood Search based on Neural Construction HeuristicsCode1
Learning to combine primitive skills: A step towards versatile robotic manipulationCode1
Latent-Variable Advantage-Weighted Policy Optimization for Offline RLCode1
Combining Reinforcement Learning with Lin-Kernighan-Helsgaun Algorithm for the Traveling Salesman ProblemCode1
Learning 2-opt Heuristics for the Traveling Salesman Problem via Deep Reinforcement LearningCode1
Learning agile and dynamic motor skills for legged robotsCode1
Learning Associative Inference Using Fast Weight MemoryCode1
Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative TasksCode1
Collective eXplainable AI: Explaining Cooperative Strategies and Agent Contribution in Multiagent Reinforcement Learning with Shapley ValuesCode1
Learning Cooperative Visual Dialog Agents with Deep Reinforcement LearningCode1
Learning Data Manipulation for Augmentation and WeightingCode1
Agent57: Outperforming the Atari Human BenchmarkCode1
Learning Domain Invariant Representations in Goal-conditioned Block MDPsCode1
An Equivalence between Loss Functions and Non-Uniform Sampling in Experience ReplayCode1
Learning for Edge-Weighted Online Bipartite Matching with Robustness GuaranteesCode1
Collision Probability Distribution Estimation via Temporal Difference LearningCode1
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Benchmark Results

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