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

Showing 14511475 of 15113 papers

TitleStatusHype
Imitation Learning via Off-Policy Distribution MatchingCode1
IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner ArchitecturesCode1
Implementation Matters in Deep RL: A Case Study on PPO and TRPOCode1
Implementing Reinforcement Learning Datacenter Congestion Control in NVIDIA NICsCode1
Accelerating Reinforcement Learning with Learned Skill PriorsCode1
Improved Representation of Asymmetrical Distances with Interval Quasimetric EmbeddingsCode1
Improving Computational Efficiency in Visual Reinforcement Learning via Stored EmbeddingsCode1
Improving Data Efficiency for LLM Reinforcement Fine-tuning Through Difficulty-targeted Online Data Selection and Rollout ReplayCode1
A Deep Reinforced Model for Zero-Shot Cross-Lingual Summarization with Bilingual Semantic Similarity RewardsCode1
Improving Model-Based Reinforcement Learning with Internal State Representations through Self-SupervisionCode1
Improving Sample Efficiency in Model-Free Reinforcement Learning from ImagesCode1
Can Q-Learning with Graph Networks Learn a Generalizable Branching Heuristic for a SAT Solver?Code1
Actor-Critic Reinforcement Learning for Control with Stability GuaranteeCode1
Inclined Quadrotor Landing using Deep Reinforcement LearningCode1
A game-theoretic analysis of networked system control for common-pool resource management using multi-agent reinforcement learningCode1
Human-centric Reward Optimization for Reinforcement Learning-based Automated Driving using Large Language ModelsCode1
Combinatorial Optimization by Graph Pointer Networks and Hierarchical Reinforcement LearningCode1
A simple but strong baseline for online continual learning: Repeated Augmented RehearsalCode1
Influencing Long-Term Behavior in Multiagent Reinforcement LearningCode1
Information Design in Multi-Agent Reinforcement LearningCode1
Asynchronous Reinforcement Learning for Real-Time Control of Physical RobotsCode1
Learning to combine primitive skills: A step towards versatile robotic manipulationCode1
Intelligent Electric Vehicle Charging Recommendation Based on Multi-Agent Reinforcement LearningCode1
Intelligent Reflecting Surface Configurations for Smart Radio Using Deep Reinforcement LearningCode1
Collaborative Multi-Agent Dialogue Model Training Via Reinforcement LearningCode1
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Benchmark Results

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