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 1851–1875 of 15113 papers

TitleStatusHype
Decentralized Semantic Traffic Control in AVs Using RL and DQN for Dynamic Roadblocks—0
Reinforcement Learning with Intrinsically Motivated Feedback Graph for Lost-sales Inventory Control—0
Preference Elicitation for Offline Reinforcement Learning—0
GenRL: Multimodal-foundation world models for generalization in embodied agentsCode2
Combining Automated Optimisation of Hyperparameters and Reward ShapeCode0
ARES: Alternating Reinforcement Learning and Supervised Fine-Tuning for Enhanced Multi-Modal Chain-of-Thought Reasoning Through Diverse AI FeedbackCode0
Leveraging Reinforcement Learning in Red Teaming for Advanced Ransomware Attack Simulations—0
The State-Action-Reward-State-Action Algorithm in Spatial Prisoner's Dilemma Game—0
EXTRACT: Efficient Policy Learning by Extracting Transferable Robot Skills from Offline Data—0
Privacy Preserving Reinforcement Learning for Population Processes—0
Human-Object Interaction from Human-Level Instructions—0
Tolerance of Reinforcement Learning Controllers against Deviations in Cyber Physical Systems—0
Decentralized RL-Based Data Transmission Scheme for Energy Efficient Harvesting—0
Reinforcement Learning via Auxiliary Task DistillationCode0
KEHRL: Learning Knowledge-Enhanced Language Representations with Hierarchical Reinforcement LearningCode0
Confidence Aware Inverse Constrained Reinforcement LearningCode0
Uncertainty-Aware Reward-Free Exploration with General Function ApproximationCode0
Memory-Enhanced Neural Solvers for Efficient Adaptation in Combinatorial OptimizationCode1
OCALM: Object-Centric Assessment with Language Models—0
Enhancing Commentary Strategies for Imperfect Information Card Games: A Study of Large Language Models in Guandan CommentaryCode0
Diffusion Spectral Representation for Reinforcement Learning—0
Decentralized Transformers with Centralized Aggregation are Sample-Efficient Multi-Agent World ModelsCode0
Multistep Criticality Search and Power Shaping in Microreactors with Reinforcement Learning—0
Direct Multi-Turn Preference Optimization for Language AgentsCode2
SiT: Symmetry-Invariant Transformers for Generalisation in Reinforcement LearningCode0
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Benchmark Results

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