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 1931–1940 of 15113 papers

TitleStatusHype
Acme: A Research Framework for Distributed Reinforcement LearningCode1
PlanGAN: Model-based Planning With Sparse Rewards and Multiple GoalsCode1
Encoding formulas as deep networks: Reinforcement learning for zero-shot execution of LTL formulasCode1
Sim2Real for Peg-Hole Insertion with Eye-in-Hand CameraCode1
Predicting Goal-directed Human Attention Using Inverse Reinforcement LearningCode1
Deep Reinforcement learning for real autonomous mobile robot navigation in indoor environmentsCode1
MOPO: Model-based Offline Policy OptimizationCode1
Modeling Penetration Testing with Reinforcement Learning Using Capture-the-Flag Challenges: Trade-offs between Model-free Learning and A Priori KnowledgeCode1
Implementation Matters in Deep Policy Gradients: A Case Study on PPO and TRPOCode1
Decentralized Deep Reinforcement Learning for a Distributed and Adaptive Locomotion Controller of a Hexapod RobotCode1
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Benchmark Results

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