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 1281–1290 of 15113 papers

TitleStatusHype
Combining Semantic Guidance and Deep Reinforcement Learning For Generating Human Level PaintingsCode1
Conservative Q-Learning for Offline Reinforcement LearningCode1
Active Exploration for Inverse Reinforcement LearningCode1
Communicative Reinforcement Learning Agents for Landmark Detection in Brain ImagesCode1
Ensemble Quantile Networks: Uncertainty-Aware Reinforcement Learning with Applications in Autonomous DrivingCode1
Comparing Popular Simulation Environments in the Scope of Robotics and Reinforcement LearningCode1
Accelerating Exploration with Unlabeled Prior DataCode1
Competitiveness of MAP-Elites against Proximal Policy Optimization on locomotion tasks in deterministic simulationsCode1
ESRL: Efficient Sampling-based Reinforcement Learning for Sequence GenerationCode1
Explainable Reinforcement Learning via a Causal World ModelCode1
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Benchmark Results

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