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 7076–7100 of 15113 papers

TitleStatusHype
HAVEN: Hierarchical Cooperative Multi-Agent Reinforcement Learning with Dual Coordination Mechanism—0
Block Contextual MDPs for Continual Learning—0
Extending Environments To Measure Self-Reflection In Reinforcement LearningCode0
Improving the sample-efficiency of neural architecture search with reinforcement learningCode0
Feudal Reinforcement Learning by Reading Manuals—0
Safe Driving via Expert Guided Policy OptimizationCode1
Reinforcement Learning for Standards Design—0
On Covariate Shift of Latent Confounders in Imitation and Reinforcement Learning—0
NeurIPS 2021 Competition IGLU: Interactive Grounded Language Understanding in a Collaborative Environment—0
Next-Best-View Estimation based on Deep Reinforcement Learning for Active Object ClassificationCode0
Power and Accountability in RL-driven Environmental Policy—0
Deciding What's Fair: Challenges of Applying Reinforcement Learning in Online Marketplaces—0
DQN-based Beamforming for Uplink mmWave Cellular-Connected UAVs—0
GridLearn: Multiagent Reinforcement Learning for Grid-Aware Building Energy Management—0
Beyond Pick-and-Place: Tackling Robotic Stacking of Diverse ShapesCode1
On Improving Model-Free Algorithms for Decentralized Multi-Agent Reinforcement Learning—0
Learning Efficient Multi-Agent Cooperative Visual Exploration—0
Provably Efficient Reinforcement Learning in Decentralized General-Sum Markov Games—0
Offline Reinforcement Learning with Implicit Q-LearningCode1
StARformer: Transformer with State-Action-Reward Representations for Visual Reinforcement LearningCode1
Temporal Abstraction in Reinforcement Learning with the Successor Representation—0
Reward-Free Model-Based Reinforcement Learning with Linear Function Approximation—0
Scalable Traffic Signal Controls using Fog-Cloud Based Multiagent Reinforcement Learning—0
An Automated Portfolio Trading System with Feature Preprocessing and Recurrent Reinforcement Learning—0
Learning from Ambiguous Demonstrations with Self-Explanation Guided Reinforcement LearningCode0
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Benchmark Results

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