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 1331–1340 of 15113 papers

TitleStatusHype
Continual Reinforcement Learning with Multi-Timescale ReplayCode1
Continuous control with deep reinforcement learningCode1
Implicit Under-Parameterization Inhibits Data-Efficient Deep Reinforcement LearningCode1
Continual World: A Robotic Benchmark For Continual Reinforcement LearningCode1
Active MR k-space Sampling with Reinforcement LearningCode1
Improving and Benchmarking Offline Reinforcement Learning AlgorithmsCode1
Continuous Deep Q-Learning with Model-based AccelerationCode1
Continuous Coordination As a Realistic Scenario for Lifelong LearningCode1
Adversarial Policies: Attacking Deep Reinforcement LearningCode1
A Deep Reinforcement Learning Approach for Solving the Traveling Salesman Problem with DroneCode1
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Benchmark Results

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