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 7726–7750 of 15113 papers

TitleStatusHype
Reinforcement Learning Agent Training with Goals for Real World Tasks—0
Proximal Policy Optimization for Tracking Control Exploiting Future Reference Information—0
Mastering Visual Continuous Control: Improved Data-Augmented Reinforcement LearningCode1
Improved Reinforcement Learning in Cooperative Multi-agent Environments Using Knowledge Transfer—0
Toward Collaborative Reinforcement Learning Agents that Communicate Through Text-Based Natural LanguageCode0
Learning Altruistic Behaviours in Reinforcement Learning without External Rewards—0
A Sustainable Ecosystem through Emergent Cooperation in Multi-Agent Reinforcement LearningCode1
Reward-Weighted Regression Converges to a Global OptimumCode0
Multimodal Reward Shaping for Efficient Exploration in Reinforcement Learning—0
Provably Efficient Multi-Task Reinforcement Learning with Model Transfer—0
Structured World Belief for Reinforcement Learning in POMDP—0
An Analysis of Reinforcement Learning for Malaria Control—0
Decoupled Reinforcement Learning to Stabilise Intrinsically-Motivated Exploration—0
Constraints Penalized Q-learning for Safe Offline Reinforcement Learning—0
A New Representation of Successor Features for Transfer across Dissimilar Environments—0
Co-designing Intelligent Control of Building HVACs and MicrogridsCode1
Vision-Based Autonomous Car Racing Using Deep Imitative Reinforcement LearningCode1
Megaverse: Simulating Embodied Agents at One Million Experiences per SecondCode1
On the Robustness of Deep Reinforcement Learning in IRS-Aided Wireless Communications Systems—0
High-Accuracy Model-Based Reinforcement Learning, a Survey—0
Hierarchical Reinforcement Learning with Optimal Level Synchronization based on a Deep Generative ModelCode0
Learning Heuristics for Template-based CEGIS of Loop Invariants with Reinforcement Learning—0
Boosting the Convergence of Reinforcement Learning-based Auto-pruning Using Historical Data—0
Geometric Value Iteration: Dynamic Error-Aware KL Regularization for Reinforcement Learning—0
Decentralized Multi-Agent Reinforcement Learning for Task Offloading Under Uncertainty—0
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Benchmark Results

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