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 2276–2300 of 15113 papers

TitleStatusHype
Learning How to Active Learn by DreamingCode0
Learning How to Actively Learn: A Deep Imitation Learning ApproachCode0
Learning Natural Language Generation with Truncated Reinforcement LearningCode0
Learning Heuristics for Quantified Boolean Formulas through Deep Reinforcement LearningCode0
Learning Goal-Oriented Visual Dialog via Tempered Policy GradientCode0
Adaptive Traffic Control with Deep Reinforcement Learning:Towards State-of-the-art and BeyondCode0
Learning Graph-Enhanced Commander-Executor for Multi-Agent NavigationCode0
Learning Heuristics over Large Graphs via Deep Reinforcement LearningCode0
Learning Generalizable Representations for Reinforcement Learning via Adaptive Meta-learner of Behavioral SimilaritiesCode0
Beating the World's Best at Super Smash Bros. with Deep Reinforcement LearningCode0
Beating Atari with Natural Language Guided Reinforcement LearningCode0
Learning Goal Embeddings via Self-Play for Hierarchical Reinforcement LearningCode0
Learning from Multiple Independent Advisors in Multi-agent Reinforcement LearningCode0
Learning from Learners: Adapting Reinforcement Learning Agents to be Competitive in a Card GameCode0
Learning from Sparse Offline Datasets via Conservative Density EstimationCode0
Learning from Demonstration without DemonstrationsCode0
Combining imitation and deep reinforcement learning to accomplish human-level performance on a virtual foraging taskCode0
Learning from Trajectories via Subgoal DiscoveryCode0
Learning from Ambiguous Demonstrations with Self-Explanation Guided Reinforcement LearningCode0
Learning Generalizable Device Placement Algorithms for Distributed Machine LearningCode0
Learning how to Active Learn: A Deep Reinforcement Learning ApproachCode0
Bayesian Robust Optimization for Imitation LearningCode0
A Biologically Plausible Learning Rule for Deep Learning in the BrainCode0
Learning data augmentation policies using augmented random searchCode0
Learning-Driven Exploration for Reinforcement LearningCode0
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Benchmark Results

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