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 6776–6800 of 15113 papers

TitleStatusHype
Towards Smarter Sensing: 2D Clutter Mitigation in RL-Driven Cognitive MIMO Radar—0
Towards Socially and Morally Aware RL agent: Reward Design With LLM—0
Towards Synthesizing Complex Programs from Input-Output Examples—0
Towards Task-Prioritized Policy Composition—0
Towards Understanding Chinese Checkers with Heuristics, Monte Carlo Tree Search, and Deep Reinforcement Learning—0
Towards Understanding Deep Policy Gradients: A Case Study on PPO—0
The Benefits of Being Categorical Distributional: Uncertainty-aware Regularized Exploration in Reinforcement Learning—0
Towards Understanding Distributional Reinforcement Learning: Regularization, Optimization, Acceleration and Sinkhorn Algorithm—0
Towards Understanding the Benefit of Multitask Representation Learning in Decision Process—0
Towards Unknown-aware Deep Q-Learning—0
Towards Unraveling and Improving Generalization in World Models—0
Fairness-Oriented User Scheduling for Bursty Downlink Transmission Using Multi-Agent Reinforcement Learning—0
Towards using Reinforcement Learning for Scaling and Data Replication in Cloud Systems—0
Towards Validating Long-Term User Feedbacks in Interactive Recommendation Systems—0
Towards Vision-Based Deep Reinforcement Learning for Robotic Motion Control—0
Towards White-box Benchmarks for Algorithm Control—0
Toward Task Generalization via Memory Augmentation in Meta-Reinforcement Learning—0
TraCeS: Trajectory Based Credit Assignment From Sparse Safety Feedback—0
TrackAgent: 6D Object Tracking via Reinforcement Learning—0
Track-Assignment Detailed Routing Using Attention-based Policy Model With Supervision—0
Tracking as Online Decision-Making: Learning a Policy from Streaming Videos with Reinforcement Learning—0
Tracking Control for a Spherical Pendulum via Curriculum Reinforcement Learning—0
Tracking the Race Between Deep Reinforcement Learning and Imitation Learning -- Extended Version—0
Track-MDP: Reinforcement Learning for Target Tracking with Controlled Sensing—0
Tractable Offline Learning of Regular Decision Processes—0
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Benchmark Results

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