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 3326–3350 of 15113 papers

TitleStatusHype
Decentralizing Multi-Agent Reinforcement Learning with Temporal Causal Information—0
Deception in Social Learning: A Multi-Agent Reinforcement Learning Perspective—0
Deep Reinforcement Learning for Single-Shot Diagnosis and Adaptation in Damaged Robots—0
Deceptive Reinforcement Learning for Privacy-Preserving Planning—0
Deceptive Reinforcement Learning in Model-Free Domains—0
Deep Reinforcement Learning for Smart Home Energy Management—0
CQM: Curriculum Reinforcement Learning with a Quantized World Model—0
Deciding What to Model: Value-Equivalent Sampling for Reinforcement Learning—0
Attention-based Fault-tolerant Approach for Multi-agent Reinforcement Learning Systems—0
Decision ConvFormer: Local Filtering in MetaFormer is Sufficient for Decision Making—0
Decision-making at Unsignalized Intersection for Autonomous Vehicles: Left-turn Maneuver with Deep Reinforcement Learning—0
Decision-making for Autonomous Vehicles on Highway: Deep Reinforcement Learning with Continuous Action Horizon—0
Decision Making in Non-Stationary Environments with Policy-Augmented Monte Carlo Tree Search—0
Decision-making Strategy on Highway for Autonomous Vehicles using Deep Reinforcement Learning—0
Attention-driven Robotic Manipulation—0
Decision Mamba: Reinforcement Learning via Hybrid Selective Sequence Modeling—0
Decision SpikeFormer: Spike-Driven Transformer for Decision Making—0
Decision Transformer for IRS-Assisted Systems with Diffusion-Driven Generative Channels—0
AgentGraph: Towards Universal Dialogue Management with Structured Deep Reinforcement Learning—0
Decision Transformers for RIS-Assisted Systems with Diffusion Model-Based Channel Acquisition—0
Decoding Molecular Graph Embeddings with Reinforcement Learning—0
Decoding Polar Codes with Reinforcement Learning—0
Decoding surface codes with deep reinforcement learning and probabilistic policy reuse—0
Attention Routing: track-assignment detailed routing using attention-based reinforcement learning—0
CPL: Critical Plan Step Learning Boosts LLM Generalization in Reasoning Tasks—0
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Benchmark Results

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