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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 9251–9275 of 15113 papers

TitleStatusHype
Reinforcement Learning in Linear MDPs: Constant Regret and Representation Selection—0
Reinforcement Learning in Low-Rank MDPs with Density Features—0
Reinforcement Learning for Economic Policy: A New Frontier?—0
Reinforcement Learning in Medical Image Analysis: Concepts, Applications, Challenges, and Future Directions—0
Reinforcement Learning in Modern Biostatistics: Constructing Optimal Adaptive Interventions—0
Reinforcement Learning in Multi-Party Trading Dialog—0
Reinforcement Learning in Newcomblike Environments—0
Reinforcement Learning in Non-Markovian Environments—0
Reinforcement Learning in Non-Markov Market-Making—0
Reinforcement Learning in Non-Stationary Environments—0
Reinforcement Learning in Non-Stationary Discrete-Time Linear-Quadratic Mean-Field Games—0
Reinforcement Learning in POMDPs with Memoryless Options and Option-Observation Initiation Sets—0
Reinforcement Learning in Presence of Discrete Markovian Context Evolution—0
Reinforcement Learning in R—0
Reinforcement Learning in Reward-Mixing MDPs—0
Reinforcement Learning in Rich-Observation MDPs using Spectral Methods—0
Reinforcement Learning in Robotic Motion Planning by Combined Experience-based Planning and Self-Imitation Learning—0
Reinforcement Learning in Robust Markov Decision Processes—0
Reinforcement Learning in Strategy-Based and Atari Games: A Review of Google DeepMinds Innovations—0
Reinforcement Learning in System Identification—0
Reinforcement Learning in the Wild: Scalable RL Dispatching Algorithm Deployed in Ridehailing Marketplace—0
Reinforcement Learning in the Wild with Maximum Likelihood-based Model Transfer—0
Demystifying Reinforcement Learning in Time-Varying Systems—0
Reinforcement Learning in Topology-based Representation for Human Body Movement with Whole Arm Manipulation—0
Reinforcement Learning Methods for Wordle: A POMDP/Adaptive Control Approach—0
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Benchmark Results

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