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 4976–5000 of 15113 papers

TitleStatusHype
Reinforcement Learning in Computing and Network Convergence Orchestration—0
Reinforcement Learning in Conflicting Environments for Autonomous Vehicles—0
Reinforcement Learning in Economics and Finance—0
Reinforcement Learning in Education: A Multi-Armed Bandit Approach—0
Reinforcement Learning in Factored Action Spaces using Tensor Decompositions—0
Deep Reinforcement Learning for FlipIt Security Game—0
Reinforcement learning informed evolutionary search for autonomous systems testing—0
Reinforcement Learning in Healthcare: A Survey—0
Reinforcement Learning in Hyperbolic Spaces: Models and Experiments—0
Reinforcement learning in large, structured action spaces: A simulation study of decision support for spinal cord injury rehabilitation—0
Reinforcement Learning in Linear MDPs: Constant Regret and Representation Selection—0
Low-Rank MDPs with Continuous Action Spaces—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
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Benchmark Results

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