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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1951–1960 of 15113 papers

TitleStatusHype
Bootstrapping Expectiles in Reinforcement Learning—0
Towards Dynamic Trend Filtering through Trend Point Detection with Reinforcement LearningCode0
ATraDiff: Accelerating Online Reinforcement Learning with Imaginary Trajectories—0
Self-Play with Adversarial Critic: Provable and Scalable Offline Alignment for Language Models—0
HackAtari: Atari Learning Environments for Robust and Continual Reinforcement LearningCode1
"Give Me an Example Like This": Episodic Active Reinforcement Learning from DemonstrationsCode0
DEER: A Delay-Resilient Framework for Reinforcement Learning with Variable Delays—0
Scaling Laws for Reward Model Overoptimization in Direct Alignment Algorithms—0
UDQL: Bridging The Gap between MSE Loss and The Optimal Value Function in Offline Reinforcement Learning—0
Fine-Grained Causal Dynamics Learning with Quantization for Improving Robustness in Reinforcement LearningCode1
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Benchmark Results

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