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

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Showing 3341–3350 of 15113 papers

TitleStatusHype
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