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 481–490 of 15113 papers

TitleStatusHype
Decision Transformer: Reinforcement Learning via Sequence ModelingCode1
Adaptive Risk-Tendency: Nano Drone Navigation in Cluttered Environments with Distributional Reinforcement LearningCode1
A Deep Reinforcement Learning Approach to First-Order Logic Theorem ProvingCode1
Controlgym: Large-Scale Control Environments for Benchmarking Reinforcement Learning AlgorithmsCode1
Decoupling Value and Policy for Generalization in Reinforcement LearningCode1
Deep Active Inference for Partially Observable MDPsCode1
DeepFreight: Integrating Deep Reinforcement Learning and Mixed Integer Programming for Multi-transfer Truck Freight DeliveryCode1
Deep Implicit Coordination Graphs for Multi-agent Reinforcement LearningCode1
A deep inverse reinforcement learning approach to route choice modeling with context-dependent rewardsCode1
Contrastive Active InferenceCode1
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Benchmark Results

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