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 2411–2420 of 15113 papers

TitleStatusHype
Performative Reinforcement Learning in Gradually Shifting EnvironmentsCode0
Jack of All Trades, Master of Some, a Multi-Purpose Transformer AgentCode2
Self-Play Fine-Tuning of Diffusion Models for Text-to-Image Generation—0
Rewards-in-Context: Multi-objective Alignment of Foundation Models with Dynamic Preference AdjustmentCode1
Towards Robust Model-Based Reinforcement Learning Against Adversarial Corruption—0
How does Your RL Agent Explore? An Optimal Transport Analysis of Occupancy Measure Trajectories—0
Steady-State Error Compensation for Reinforcement Learning with Quadratic Rewards—0
Exploiting Estimation Bias in Clipped Double Q-Learning for Continous Control Reinforcement Learning Tasks—0
Discovering Command and Control (C2) Channels on Tor and Public Networks Using Reinforcement Learning—0
Conservative and Risk-Aware Offline Multi-Agent Reinforcement LearningCode0
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Benchmark Results

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