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 1951–1975 of 15113 papers

TitleStatusHype
Beyond Uniform Sampling: Offline Reinforcement Learning with Imbalanced DatasetsCode1
Beyond The Rainbow: High Performance Deep Reinforcement Learning on a Desktop PCCode1
Hierarchical Learning-based Graph Partition for Large-scale Vehicle Routing ProblemsCode1
Sim2Real Transfer for Reinforcement Learning without Dynamics RandomizationCode1
Hierarchical Reinforcement Learning for Power Network Topology ControlCode1
Bidirectional Model-based Policy OptimizationCode1
Bayesian Soft Actor-Critic: A Directed Acyclic Strategy Graph Based Deep Reinforcement LearningCode1
Hierarchical Reinforcement Learning By Discovering Intrinsic OptionsCode1
Hierarchical Skills for Efficient ExplorationCode1
HiMAP: Learning Heuristics-Informed Policies for Large-Scale Multi-Agent PathfindingCode1
Inferring Rewards from Language in ContextCode1
Hierarchical Reinforcement Learning with Timed SubgoalsCode1
Intelligent Electric Vehicle Charging Recommendation Based on Multi-Agent Reinforcement LearningCode1
Investigating practical linear temporal difference learningCode1
A Multiplicative Value Function for Safe and Efficient Reinforcement LearningCode1
High-Throughput Synchronous Deep RLCode1
ABIDES-Gym: Gym Environments for Multi-Agent Discrete Event Simulation and Application to Financial MarketsCode1
Hindsight Experience ReplayCode1
Beyond Greedy Search: Tracking by Multi-Agent Reinforcement Learning-based Beam SearchCode1
How Consistent are Clinicians? Evaluating the Predictability of Sepsis Disease Progression with Dynamics ModelsCode1
An actor-critic algorithm with policy gradients to solve the job shop scheduling problem using deep double recurrent agentsCode1
Hindsight Preference Learning for Offline Preference-based Reinforcement LearningCode1
Can Q-Learning with Graph Networks Learn a Generalizable Branching Heuristic for a SAT Solver?Code1
Batch Exploration with Examples for Scalable Robotic Reinforcement LearningCode1
Beyond OOD State Actions: Supported Cross-Domain Offline Reinforcement LearningCode1
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Benchmark Results

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