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 751–760 of 15113 papers

TitleStatusHype
Curiosity-Driven Energy-Efficient Worker Scheduling in Vehicular Crowdsourcing: A Deep Reinforcement Learning ApproachCode1
Accelerating lifelong reinforcement learning via reshaping rewardsCode1
Adversarial Policies: Attacking Deep Reinforcement LearningCode1
Deep Reinforcement Learning with Double Q-learningCode1
Content Masked Loss: Human-Like Brush Stroke Planning in a Reinforcement Learning Painting AgentCode1
Constructions in combinatorics via neural networksCode1
Active MR k-space Sampling with Reinforcement LearningCode1
Deep RL Agent for a Real-Time Action Strategy GameCode1
Deep Transformer Q-Networks for Partially Observable Reinforcement LearningCode1
Contention Window Optimization in IEEE 802.11ax Networks with Deep Reinforcement LearningCode1
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Benchmark Results

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