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 6151–6175 of 15113 papers

TitleStatusHype
Learning to Scan: A Deep Reinforcement Learning Approach for Personalized Scanning in CT Imaging—0
Learning to Schedule Heuristics for the Simultaneous Stochastic Optimization of Mining Complexes—0
Learning to schedule job-shop problems: Representation and policy learning using graph neural network and reinforcement learning—0
Learning to Select the Next Reasonable Mention for Entity Linking—0
Learning to Shape Rewards using a Game of Two Partners—0
Learning to Shoot in First Person Shooter Games by Stabilizing Actions and Clustering Rewards for Reinforcement Learning—0
Learning to Sit: Synthesizing Human-Chair Interactions via Hierarchical Control—0
Learning to solve arithmetic problems with a virtual abacus—0
Learning To Solve Circuit-SAT: An Unsupervised Differentiable Approach—0
Learning to Optimise Climate Sensor Placement using a Transformer—0
Learning to Solve Combinatorial Problems via Efficient Exploration—0
Learning to superoptimize programs - Workshop Version—0
Learning to Switch Among Agents in a Team via 2-Layer Markov Decision Processes—0
Learning to Teach in Cooperative Multiagent Reinforcement Learning—0
Learning to Teach Reinforcement Learning Agents—0
Learning to Transfer Learn: Reinforcement Learning-Based Selection for Adaptive Transfer Learning—0
Learning to Transfer Role Assignment Across Team Sizes—0
Learning to Treat Sepsis with Multi-Output Gaussian Process Deep Recurrent Q-Networks—0
Learning to Unknot—0
Learning to Utilize Shaping Rewards: A New Approach of Reward Shaping—0
Learning to View: Decision Transformers for Active Object Detection—0
Learning to Walk: Spike Based Reinforcement Learning for Hexapod Robot Central Pattern Generation—0
Learning to Walk via Deep Reinforcement Learning—0
Learning Transferable Domain Priors for Safe Exploration in Reinforcement Learning—0
Learning Transition Models with Time-delayed Causal Relations—0
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Benchmark Results

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