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 3281–3290 of 15113 papers

TitleStatusHype
Confidence-Controlled Exploration: Efficient Sparse-Reward Policy Learning for Robot Navigation—0
The Role of Diverse Replay for Generalisation in Reinforcement Learning—0
On the Importance of Feature Decorrelation for Unsupervised Representation Learning in Reinforcement LearningCode1
Bring Your Own (Non-Robust) Algorithm to Solve Robust MDPs by Estimating The Worst Kernel—0
Approximate information state based convergence analysis of recurrent Q-learning—0
Learning Not to Spoof—0
Iteratively Refined Behavior Regularization for Offline Reinforcement Learning—0
An End-to-End Reinforcement Learning Approach for Job-Shop Scheduling Problems Based on Constraint ProgrammingCode1
Decoupled Prioritized Resampling for Offline RLCode1
Instructed Diffuser with Temporal Condition Guidance for Offline Reinforcement Learning—0
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Benchmark Results

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