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 1841–1850 of 15113 papers

TitleStatusHype
Reinforcement Learning for Ballbot Navigation in Uneven TerrainCode1
Reinforcement Learning for Branch-and-Bound Optimisation using Retrospective TrajectoriesCode1
Reinforcement Learning for Dynamic Resource Allocation in Optical Networks: Hype or Hope?Code1
Reinforcement learning for Energies of the future and carbon neutrality: a Challenge DesignCode1
Combining Deep Reinforcement Learning and Search for Imperfect-Information GamesCode1
Reinforcement Learning for Low-Thrust Trajectory Design of Interplanetary MissionsCode1
Combinatorial Optimization with Policy Adaptation using Latent Space SearchCode1
AMAGO: Scalable In-Context Reinforcement Learning for Adaptive AgentsCode1
Reinforcement Learning for Adaptive Optimal Stationary Control of Linear Stochastic SystemsCode1
Learning to combine primitive skills: A step towards versatile robotic manipulationCode1
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Benchmark Results

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