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 2491–2500 of 15113 papers

TitleStatusHype
ODICE: Revealing the Mystery of Distribution Correction Estimation via Orthogonal-gradient UpdateCode1
Towards Efficient Exact Optimization of Language Model AlignmentCode2
Developing A Multi-Agent and Self-Adaptive Framework with Deep Reinforcement Learning for Dynamic Portfolio Risk ManagementCode2
Leveraging Approximate Model-based Shielding for Probabilistic Safety Guarantees in Continuous EnvironmentsCode0
Safe Reinforcement Learning-Based Eco-Driving Control for Mixed Traffic Flows With Disturbances—0
A Reinforcement Learning Based Controller to Minimize Forces on the Crutches of a Lower-Limb Exoskeleton—0
A Policy Gradient Primal-Dual Algorithm for Constrained MDPs with Uniform PAC GuaranteesCode0
Causal Coordinated Concurrent Reinforcement Learning—0
Attention Graph for Multi-Robot Social Navigation with Deep Reinforcement Learning—0
Zero-Shot Reinforcement Learning via Function EncodersCode0
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Benchmark Results

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