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

Showing 19111920 of 15113 papers

TitleStatusHype
SAMBA: Safe Model-Based & Active Reinforcement LearningCode1
TorsionNet: A Reinforcement Learning Approach to Sequential Conformer SearchCode1
Closed Loop Neural-Symbolic Learning via Integrating Neural Perception, Grammar Parsing, and Symbolic ReasoningCode1
Modelling Hierarchical Structure between Dialogue Policy and Natural Language Generator with Option Framework for Task-oriented Dialogue SystemCode1
What Matters In On-Policy Reinforcement Learning? A Large-Scale Empirical StudyCode1
Robust Spammer Detection by Nash Reinforcement LearningCode1
Learning to Incentivize Other Learning AgentsCode1
Constrained episodic reinforcement learning in concave-convex and knapsack settingsCode1
Learning to Play No-Press Diplomacy with Best Response Policy IterationCode1
Reinforcement Learning Under Moral UncertaintyCode1
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Benchmark Results

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