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 7276–7300 of 15113 papers

TitleStatusHype
What's Behind PPO's Collapse in Long-CoT? Value Optimization Holds the Secret—0
What Should I Do Now? Marrying Reinforcement Learning and Symbolic Planning—0
What Would pi* Do?: Imitation Learning via Off-Policy Reinforcement Learning—0
(When) Are Contrastive Explanations of Reinforcement Learning Helpful?—0
When Autonomous Systems Meet Accuracy and Transferability through AI: A Survey—0
When Can Large Reasoning Models Save Thinking? Mechanistic Analysis of Behavioral Divergence in Reasoning—0
When Collaborative Filtering Meets Reinforcement Learning—0
When Do Drivers Concentrate? Attention-based Driver Behavior Modeling With Deep Reinforcement Learning—0
When is Agnostic Reinforcement Learning Statistically Tractable?—0
When is a Prediction Knowledge?—0
When Is Generalizable Reinforcement Learning Tractable?—0
When is Offline Two-Player Zero-Sum Markov Game Solvable?—0
When Is Partially Observable Reinforcement Learning Not Scary?—0
When is Realizability Sufficient for Off-Policy Reinforcement Learning?—0
When Learning Is Out of Reach, Reset: Generalization in Autonomous Visuomotor Reinforcement Learning—0
When Mining Electric Locomotives Meet Reinforcement Learning—0
When Multiple Agents Learn to Schedule: A Distributed Radio Resource Management Framework—0
Provably Robust Blackbox Optimization for Reinforcement Learning—0
When should agents explore?—0
When Should We Prefer Offline Reinforcement Learning Over Behavioral Cloning?—0
When Simple Exploration is Sample Efficient: Identifying Sufficient Conditions for Random Exploration to Yield PAC RL Algorithms—0
When to Go, and When to Explore: The Benefit of Post-Exploration in Intrinsic Motivation—0
When to Localize? A Risk-Constrained Reinforcement Learning Approach—0
When to Trust Your Data: Enhancing Dyna-Style Model-Based Reinforcement Learning With Data Filter—0
Membership Inference Attacks Against Temporally Correlated Data in Deep Reinforcement Learning—0
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Benchmark Results

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