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 3551–3560 of 15113 papers

TitleStatusHype
Mitigating Partial Observability in Adaptive Traffic Signal Control with Transformers—0
Logic Synthesis Optimization with Predictive Self-Supervision via Causal Transformers—0
An Offline Adaptation Framework for Constrained Multi-Objective Reinforcement Learning—0
Mitigating Dimensionality in 2D Rectangle Packing Problem under Reinforcement Learning Schema—0
KAN v.s. MLP for Offline Reinforcement Learning—0
PIP-Loco: A Proprioceptive Infinite Horizon Planning Framework for Quadrupedal Robot Locomotion—0
Batch Ensemble for Variance Dependent Regret in Stochastic Bandits—0
Average-Reward Maximum Entropy Reinforcement Learning for Underactuated Double Pendulum Tasks—0
CPL: Critical Plan Step Learning Boosts LLM Generalization in Reasoning Tasks—0
Quasimetric Value Functions with Dense Rewards—0
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Benchmark Results

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