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 1211–1220 of 15113 papers

TitleStatusHype
Accelerated Sim-to-Real Deep Reinforcement Learning: Learning Collision Avoidance from Human PlayerCode1
Deep reinforcement learning for large-scale epidemic controlCode1
Compound AI Systems Optimization: A Survey of Methods, Challenges, and Future DirectionsCode1
Deep Reinforcement Learning for Market Making Under a Hawkes Process-Based Limit Order Book ModelCode1
Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative TasksCode1
Communicative Reinforcement Learning Agents for Landmark Detection in Brain ImagesCode1
Deep Reinforcement Learning for Solving the Heterogeneous Capacitated Vehicle Routing ProblemCode1
AnyBipe: An End-to-End Framework for Training and Deploying Bipedal Robots Guided by Large Language ModelsCode1
Comparing Observation and Action Representations for Deep Reinforcement Learning in μRTSCode1
Combining Semantic Guidance and Deep Reinforcement Learning For Generating Human Level PaintingsCode1
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Benchmark Results

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