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 3211–3220 of 15113 papers

TitleStatusHype
A Framework for dynamically meeting performance objectives on a service mesh—0
Is RLHF More Difficult than Standard RL?—0
Fighting Uncertainty with Gradients: Offline Reinforcement Learning via Diffusion Score Matching—0
Towards Optimal Pricing of Demand Response -- A Nonparametric Constrained Policy Optimization Approach—0
Reinforcement Learning with Temporal-Logic-Based Causal Diagrams—0
Active Coverage for PAC Reinforcement Learning—0
CLUE: Calibrated Latent Guidance for Offline Reinforcement Learning—0
Harnessing Mixed Offline Reinforcement Learning Datasets via Trajectory WeightingCode1
MP3: Movement Primitive-Based (Re-)Planning Policy—0
Transferable Curricula through Difficulty Conditioned Generators—0
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Benchmark Results

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