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 2101–2110 of 15113 papers

TitleStatusHype
Controlgym: Large-Scale Control Environments for Benchmarking Reinforcement Learning AlgorithmsCode1
Control-Informed Reinforcement Learning for Chemical ProcessesCode1
Combining Reinforcement Learning and Constraint Programming for Combinatorial OptimizationCode1
Combining Modular Skills in Multitask LearningCode1
COOL-MC: A Comprehensive Tool for Reinforcement Learning and Model CheckingCode1
Cooperative Multi-Agent Reinforcement Learning with Sequential Credit AssignmentCode1
CORA: Benchmarks, Baselines, and Metrics as a Platform for Continual Reinforcement Learning AgentsCode1
Co-Reinforcement Learning for Unified Multimodal Understanding and GenerationCode1
Combining Reinforcement Learning with Lin-Kernighan-Helsgaun Algorithm for the Traveling Salesman ProblemCode1
Combining Deep Reinforcement Learning and Search for Imperfect-Information GamesCode1
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Benchmark Results

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