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 3801–3825 of 15113 papers

TitleStatusHype
Maneuver Decision-Making For Autonomous Air Combat Through Curriculum Learning And Reinforcement Learning With Sparse Rewards—0
ReMIX: Regret Minimization for Monotonic Value Function Factorization in Multiagent Reinforcement Learning—0
Procedural generation of meta-reinforcement learning tasksCode1
Cross-domain Random Pre-training with Prototypes for Reinforcement LearningCode0
Towards Minimax Optimality of Model-based Robust Reinforcement Learning—0
A SWAT-based Reinforcement Learning Framework for Crop ManagementCode1
A Survey on Causal Reinforcement Learning—0
The Wisdom of Hindsight Makes Language Models Better Instruction FollowersCode1
Low Entropy Communication in Multi-Agent Reinforcement Learning—0
Combining Reconstruction and Contrastive Methods for Multimodal Representations in RLCode0
On Penalty-based Bilevel Gradient Descent MethodCode1
Learning Complex Teamwork Tasks Using a Given Sub-task DecompositionCode0
RayNet: A Simulation Platform for Developing Reinforcement Learning-Driven Network ProtocolsCode1
Data Quality-aware Mixed-precision Quantization via Hybrid Reinforcement Learning—0
Scaling Goal-based Exploration via Pruning Proto-goals—0
Hierarchical Generative Adversarial Imitation Learning with Mid-level Input Generation for Autonomous Driving on Urban EnvironmentsCode1
ManiSkill2: A Unified Benchmark for Generalizable Manipulation SkillsCode1
CLARE: Conservative Model-Based Reward Learning for Offline Inverse Reinforcement Learning—0
Equivariant MuZero—0
An Investigation into Pre-Training Object-Centric Representations for Reinforcement Learning—0
AISYN: AI-driven Reinforcement Learning-Based Logic Synthesis Framework—0
A Scale-Independent Multi-Objective Reinforcement Learning with Convergence Analysis—0
Efficient Planning in Combinatorial Action Spaces with Applications to Cooperative Multi-Agent Reinforcement Learning—0
A Near-Optimal Algorithm for Safe Reinforcement Learning Under Instantaneous Hard Constraints—0
Near-Optimal Adversarial Reinforcement Learning with Switching Costs—0
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Benchmark Results

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