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 10651–10675 of 15113 papers

TitleStatusHype
TauRieL: Targeting Traveling Salesman Problem with a deep reinforcement learning inspired architecture—0
Taxable Stock Trading with Deep Reinforcement Learning—0
Taylor Expansion of Discount Factors—0
Taylor Expansion Policy Optimization—0
TBQ(σ): Improving Efficiency of Trace Utilization for Off-Policy Reinforcement Learning—0
T-Cell Receptor Optimization with Reinforcement Learning and Mutation Policies for Precesion Immunotherapy—0
Teacher-Critical Training Strategies for Image Captioning—0
Teacher-student curriculum learning for reinforcement learning—0
Teaching a Robot to Walk Using Reinforcement Learning—0
Teaching GANs to Sketch in Vector Format—0
Teaching LLMs for Step-Level Automatic Math Correction via Reinforcement Learning—0
Teaching robots to perceive time -- A reinforcement learning approach (Extended version)—0
TEA: Trajectory Encoding Augmentation for Robust and Transferable Policies in Offline Reinforcement Learning—0
Technical Report: Adaptive Control for Linearizable Systems Using On-Policy Reinforcement Learning—0
Technical Report on Reinforcement Learning Control on the Lucas-Nülle Inverted Pendulum—0
Techniques for Automated Machine Learning—0
Techniques Toward Optimizing Viewability in RTB Ad Campaigns Using Reinforcement Learning—0
Temporal Abstraction in Reinforcement Learning with the Successor Representation—0
Temporal-adaptive Hierarchical Reinforcement Learning—0
Temporal Complementarity-Guided Reinforcement Learning for Image-to-Video Person Re-Identification—0
Temporal Difference Based Actor Critic Learning - Convergence and Neural Implementation—0
Temporal-Difference Learning to Assist Human Decision Making during the Control of an Artificial Limb—0
Temporal Difference Learning with Compressed Updates: Error-Feedback meets Reinforcement Learning—0
Temporal Difference Learning with Experience Replay—0
Temporal Difference Models: Model-Free Deep RL for Model-Based Control—0
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Benchmark Results

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