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 771780 of 15113 papers

TitleStatusHype
Contention Window Optimization in IEEE 802.11ax Networks with Deep Reinforcement LearningCode1
Context-aware Dynamics Model for Generalization in Model-Based Reinforcement LearningCode1
Adversarially Trained Actor Critic for Offline Reinforcement LearningCode1
Constructions in combinatorics via neural networksCode1
Contextualized Rewriting for Text SummarizationCode1
Continual Model-Based Reinforcement Learning with HypernetworksCode1
Constrained Policy Optimization via Bayesian World ModelsCode1
Diffusion Policies creating a Trust Region for Offline Reinforcement LearningCode1
Constrained episodic reinforcement learning in concave-convex and knapsack settingsCode1
Constrained Update Projection Approach to Safe Policy OptimizationCode1
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Benchmark Results

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