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 3321–3330 of 15113 papers

TitleStatusHype
Deep Reinforcement Learning for Robotic Manipulation with Asynchronous Off-Policy Updates—0
Decentralized Semantic Traffic Control in AVs Using RL and DQN for Dynamic Roadblocks—0
CQM: Curriculum Reinforcement Learning with a Quantized World Model—0
Nearest-Neighbor-based Collision Avoidance for Quadrotors via Reinforcement Learning—0
Attention-Aware Face Hallucination via Deep Reinforcement Learning—0
Decentralizing Multi-Agent Reinforcement Learning with Temporal Causal Information—0
AgentGraph: Towards Universal Dialogue Management with Structured Deep Reinforcement Learning—0
CPL: Critical Plan Step Learning Boosts LLM Generalization in Reasoning Tasks—0
Deceptive Reinforcement Learning for Privacy-Preserving Planning—0
C-Planning: An Automatic Curriculum for Learning Goal-Reaching Tasks—0
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Benchmark Results

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