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 3801–3810 of 15113 papers

TitleStatusHype
Maneuver Decision-Making For Autonomous Air Combat Through Curriculum Learning And Reinforcement Learning With Sparse Rewards—0
Procedural generation of meta-reinforcement learning tasksCode1
ReMIX: Regret Minimization for Monotonic Value Function Factorization in Multiagent Reinforcement Learning—0
Cross-domain Random Pre-training with Prototypes for Reinforcement LearningCode0
Combining Reconstruction and Contrastive Methods for Multimodal Representations in RLCode0
A SWAT-based Reinforcement Learning Framework for Crop ManagementCode1
A Survey on Causal Reinforcement Learning—0
Towards Minimax Optimality of Model-based Robust Reinforcement Learning—0
On Penalty-based Bilevel Gradient Descent MethodCode1
The Wisdom of Hindsight Makes Language Models Better Instruction FollowersCode1
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Benchmark Results

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