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 3351–3360 of 15113 papers

TitleStatusHype
CQM: Curriculum Reinforcement Learning with a Quantized World Model—0
AgentGraph: Towards Universal Dialogue Management with Structured Deep Reinforcement Learning—0
Attitude Control of Highly Maneuverable Aircraft Using an Improved Q-learning—0
Decomposing the Prediction Problem; Autonomous Navigation by neoRL Agents—0
Decomposition of Uncertainty in Bayesian Deep Learning for Efficient and Risk-sensitive Learning—0
Attraction-Repulsion Actor-Critic for Continuous Control Reinforcement Learning—0
CPL: Critical Plan Step Learning Boosts LLM Generalization in Reasoning Tasks—0
DECORE: Deep Compression with Reinforcement Learning—0
C-Planning: An Automatic Curriculum for Learning Goal-Reaching Tasks—0
A Study on Dense and Sparse (Visual) Rewards in Robot Policy Learning—0
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Benchmark Results

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