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 2131–2140 of 15113 papers

TitleStatusHype
When to use parametric models in reinforcement learning?Code1
Boosting Soft Actor-Critic: Emphasizing Recent Experience without Forgetting the PastCode1
Snooping Attacks on Deep Reinforcement LearningCode1
Coordinated Exploration via Intrinsic Rewards for Multi-Agent Reinforcement LearningCode1
SQIL: Imitation Learning via Reinforcement Learning with Sparse RewardsCode1
Adversarial Policies: Attacking Deep Reinforcement LearningCode1
Maximum Entropy-Regularized Multi-Goal Reinforcement LearningCode1
QTRAN: Learning to Factorize with Transformation for Cooperative Multi-Agent Reinforcement LearningCode1
Challenges of Real-World Reinforcement LearningCode1
Model-free Deep Reinforcement Learning for Urban Autonomous DrivingCode1
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Benchmark Results

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