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 4751–4775 of 15113 papers

TitleStatusHype
JAX-LOB: A GPU-Accelerated limit order book simulator to unlock large scale reinforcement learning for trading—0
Continuous Reinforcement Learning-based Dynamic Difficulty Adjustment in a Visual Working Memory Game—0
Bayesian Exploration Networks—0
Extreme Risk Mitigation in Reinforcement Learning using Extreme Value Theory—0
Conditional Kernel Imitation Learning for Continuous State Environments—0
MolOpt: Autonomous Molecular Geometry Optimization using Multi-Agent Reinforcement LearningCode0
Reinforcement learning informed evolutionary search for autonomous systems testing—0
Racing Towards Reinforcement Learning based control of an Autonomous Formula SAE Car—0
RamseyRL: A Framework for Intelligent Ramsey Number Counterexample SearchingCode0
Aligning Language Models with Offline Learning from Human Feedback—0
Towards Validating Long-Term User Feedbacks in Interactive Recommendation Systems—0
Stabilizing Unsupervised Environment Design with a Learned Adversary—0
A Homogenization Approach for Gradient-Dominated Stochastic Optimization—0
LaGR-SEQ: Language-Guided Reinforcement Learning with Sample-Efficient QueryingCode0
Soft Decomposed Policy-Critic: Bridging the Gap for Effective Continuous Control with Discrete RL—0
Accelerating Exact Combinatorial Optimization via RL-based Initialization -- A Case Study in Scheduling—0
UAV-assisted Semantic Communication with Hybrid Action Reinforcement Learning—0
Improving Sample Efficiency of Model-Free Algorithms for Zero-Sum Markov Games—0
ReProHRL: Towards Multi-Goal Navigation in the Real World using Hierarchical Agents—0
Reinforced Self-Training (ReST) for Language Modeling—0
Data-driven Integrated Sensing and Communication: Recent Advances, Challenges, and Future Prospects—0
IMM: An Imitative Reinforcement Learning Approach with Predictive Representation Learning for Automatic Market Making—0
Partially Observable Multi-Agent Reinforcement Learning with Information Sharing—0
On-demand Cold Start Frequency Reduction with Off-Policy Reinforcement Learning in Serverless Computing—0
Planning to Learn: A Novel Algorithm for Active Learning during Model-Based PlanningCode0
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Benchmark Results

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