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 4001–4010 of 15113 papers

TitleStatusHype
Zero-Sum Positional Differential Games as a Framework for Robust Reinforcement Learning: Deep Q-Learning Approach—0
Model-based reinforcement learning for protein backbone design—0
Reinforcement Learning-Guided Semi-Supervised Learning—0
Plan-Seq-Learn: Language Model Guided RL for Solving Long Horizon Robotics Tasks—0
Robust Risk-Sensitive Reinforcement Learning with Conditional Value-at-Risk—0
FLAME: Factuality-Aware Alignment for Large Language Models—0
Learning Force Control for Legged Manipulation—0
Constrained Reinforcement Learning Under Model Mismatch—0
Reinforcement Learning for Edit-Based Non-Autoregressive Neural Machine Translation—0
Tabular and Deep Reinforcement Learning for Gittins Index—0
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Benchmark Results

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