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 5326–5350 of 15113 papers

TitleStatusHype
Phasic Self-Imitative Reduction for Sparse-Reward Goal-Conditioned Reinforcement Learning—0
Reinforcement Learning under Partial Observability Guided by Learned Environment Models—0
Recursive Reinforcement Learning—0
The Real Deal: A Review of Challenges and Opportunities in Moving Reinforcement Learning-Based Traffic Signal Control Systems Towards Reality—0
Nearly Minimax Optimal Reinforcement Learning with Linear Function Approximation—0
Video PreTraining (VPT): Learning to Act by Watching Unlabeled Online VideosCode3
Learning Agile Skills via Adversarial Imitation of Rough Partial Demonstrations—0
CGAR: Critic Guided Action Redistribution in Reinforcement LeaningCode0
A Federated Reinforcement Learning Method with Quantization for Cooperative Edge Caching in Fog Radio Access Networks—0
Multi-Agent Car Parking using Reinforcement LearningCode1
Optimistic Linear Support and Successor Features as a Basis for Optimal Policy TransferCode0
Constrained Stochastic Nonconvex Optimization with State-dependent Markov Data—0
PAC: Assisted Value Factorisation with Counterfactual Predictions in Multi-Agent Reinforcement LearningCode0
Auto-Encoding Adversarial Imitation Learning—0
Multi-Horizon Representations with Hierarchical Forward Models for Reinforcement LearningCode0
Decentralized Gossip-Based Stochastic Bilevel Optimization over Communication Networks—0
Curious Exploration via Structured World Models Yields Zero-Shot Object Manipulation—0
Fusion of Model-free Reinforcement Learning with Microgrid Control: Review and Vision—0
Learning Optimal Treatment Strategies for Sepsis Using Offline Reinforcement Learning in Continuous Space—0
Deep Reinforcement Learning for Turbulence Modeling in Large Eddy SimulationsCode1
Imitate then Transcend: Multi-Agent Optimal Execution with Dual-Window Denoise PPO—0
Meta Reinforcement Learning with Finite Training Tasks -- a Density Estimation ApproachCode0
On the Statistical Efficiency of Reward-Free Exploration in Non-Linear RL—0
Robust Task Representations for Offline Meta-Reinforcement Learning via Contrastive LearningCode1
Safe and Psychologically Pleasant Traffic Signal Control with Reinforcement Learning using Action Masking—0
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Benchmark Results

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