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 11226–11250 of 15113 papers

TitleStatusHype
Unified Locomotion Transformer with Simultaneous Sim-to-Real Transfer for Quadrupeds—0
Unified Policy Optimization for Continuous-action Reinforcement Learning in Non-stationary Tasks and Games—0
Unified Reinforcement Q-Learning for Mean Field Game and Control Problems—0
Uniform-PAC Bounds for Reinforcement Learning with Linear Function Approximation—0
Uniform-PAC Guarantees for Model-Based RL with Bounded Eluder Dimension—0
Uniform State Abstraction For Reinforcement Learning—0
Unifying Causal Inference and Reinforcement Learning using Higher-Order Category Theory—0
Unifying Ensemble Methods for Q-learning via Social Choice Theory—0
Unifying task specification in reinforcement learning—0
Unifying Value Iteration, Advantage Learning, and Dynamic Policy Programming—0
Universal Activation Function For Machine Learning—0
Universal Agent for Disentangling Environments and Tasks—0
Universal Agent Mixtures and the Geometry of Intelligence—0
Universal Distributional Decision-based Black-box Adversarial Attack with Reinforcement Learning—0
Universal Learning Waveform Selection Strategies for Adaptive Target Tracking—0
Universal Successor Features Based Deep Reinforcement Learning for Navigation—0
Universal Successor Features for Transfer Reinforcement Learning—0
Universal Successor Representations for Transfer Reinforcement Learning—0
Universal Trading for Order Execution with Oracle Policy Distillation—0
UniVG-R1: Reasoning Guided Universal Visual Grounding with Reinforcement Learning—0
UniZero: Generalized and Efficient Planning with Scalable Latent World Models—0
Unlearning Works Better Than You Think: Local Reinforcement-Based Selection of Auxiliary Objectives—0
Unleashing the Reasoning Potential of Pre-trained LLMs by Critique Fine-Tuning on One Problem—0
Unlocking Pixels for Reinforcement Learning via Implicit Attention—0
Unlocking the Potential of Simulators: Design with RL in Mind—0
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Benchmark Results

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