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 2301–2325 of 15113 papers

TitleStatusHype
Learning Goal-Oriented Visual Dialog via Tempered Policy GradientCode0
Analysis and Control of a Planar QuadrotorCode0
Learning Graph-Enhanced Commander-Executor for Multi-Agent NavigationCode0
Adaptive teachers for amortized samplersCode0
Learning Goal Embeddings via Self-Play for Hierarchical Reinforcement LearningCode0
Learning How to Active Learn by DreamingCode0
Bayesian Optimization with Robust Bayesian Neural NetworksCode0
Bayesian Optimization for Iterative LearningCode0
Learning from Trajectories via Subgoal DiscoveryCode0
Bayesian Nonparametrics for Offline Skill DiscoveryCode0
Adaptive Symmetric Reward Noising for Reinforcement LearningCode0
Learning from Sparse Offline Datasets via Conservative Density EstimationCode0
Learning Generalizable Device Placement Algorithms for Distributed Machine LearningCode0
MAP Propagation Algorithm: Faster Learning with a Team of Reinforcement Learning AgentsCode0
Bayesian Inverse Reinforcement Learning for Collective Animal MovementCode0
Learning from Learners: Adapting Reinforcement Learning Agents to be Competitive in a Card GameCode0
Bayesian Inference with Anchored Ensembles of Neural Networks, and Application to Exploration in Reinforcement LearningCode0
A Comparison of Reward Functions in Q-Learning Applied to a Cart Position ProblemCode0
Learning from Multiple Independent Advisors in Multi-agent Reinforcement LearningCode0
Learning Generalizable Representations for Reinforcement Learning via Adaptive Meta-learner of Behavioral SimilaritiesCode0
Learning How to Actively Learn: A Deep Imitation Learning ApproachCode0
Learning Fair Policies in Multiobjective (Deep) Reinforcement Learning with Average and Discounted RewardsCode0
Learning from Ambiguous Demonstrations with Self-Explanation Guided Reinforcement LearningCode0
A Comparison of Reinforcement Learning Frameworks for Software Testing TasksCode0
Bayesian Design Principles for Offline-to-Online Reinforcement LearningCode0
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Benchmark Results

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