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 3921–3930 of 15113 papers

TitleStatusHype
SNeRL: Semantic-aware Neural Radiance Fields for Reinforcement Learning—0
Exploring Deep Reinforcement Learning for Holistic Smart Building Control—0
Solving Richly Constrained Reinforcement Learning through State Augmentation and Reward Penalties—0
Outcome-directed Reinforcement Learning by Uncertainty & Temporal Distance-Aware Curriculum Goal GenerationCode1
Reinforcement Learning from Diverse Human Preferences—0
Modeling human road crossing decisions as reward maximization with visual perception limitations—0
Generalized Munchausen Reinforcement Learning using Tsallis KL Divergence—0
Improving Behavioural Cloning with Positive Unlabeled Learning—0
Certifiably Robust Reinforcement Learning through Model-Based Abstract Interpretation—0
Model-based Offline Reinforcement Learning with Local Misspecification—0
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Benchmark Results

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