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 6601–6625 of 15113 papers

TitleStatusHype
Tight Finite Time Bounds of Two-Time-Scale Linear Stochastic Approximation with Markovian Noise—0
Tight Guarantees for Interactive Decision Making with the Decision-Estimation Coefficient—0
Tile Networks: Learning Optimal Geometric Layout for Whole-page Recommendation—0
Time Adaptive Reinforcement Learning—0
Time-Aware Q-Networks: Resolving Temporal Irregularity for Deep Reinforcement Learning—0
Efficient Scheduling of Data Augmentation for Deep Reinforcement Learning—0
Time-Scale Separation in Q-Learning: Extending TD() for Action-Value Function Decomposition—0
Time-Variant Variational Transfer for Value Functions—0
Time your hedge with Deep Reinforcement Learning—0
Timing is Everything: Learning to Act Selectively with Costly Actions and Budgetary Constraints—0
Timing Process Interventions with Causal Inference and Reinforcement Learning—0
tinyMAN: Lightweight Energy Manager using Reinforcement Learning for Energy Harvesting Wearable IoT Devices—0
To Beam Or Not To Beam: That is a Question of Cooperation for Language GANs—0
To bootstrap or to rollout? An optimal and adaptive interpolation—0
To Combine or Not To Combine? A Rainbow Deep Reinforcement Learning Agent for Dialog Policies—0
Toddler-Guidance Learning: Impacts of Critical Period on Multimodal AI Agents—0
Together We Rise: Optimizing Real-Time Multi-Robot Task Allocation using Coordinated Heterogeneous Plays—0
Toggling a Genetic Switch Using Reinforcement Learning—0
Token-Efficient RL for LLM Reasoning—0
Token-Mol 1.0: Tokenized drug design with large language model—0
Tolerance of Reinforcement Learning Controllers against Deviations in Cyber Physical Systems—0
TOMA: Topological Map Abstraction for Reinforcement Learning—0
Toolpath design for additive manufacturing using deep reinforcement learning—0
Topic-Preserving Synthetic News Generation: An Adversarial Deep Reinforcement Learning Approach—0
To Risk or Not to Risk: Learning with Risk Quantification for IoT Task Offloading in UAVs—0
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Benchmark Results

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