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 3151–3160 of 15113 papers

TitleStatusHype
Seeing is not Believing: Robust Reinforcement Learning against Spurious Correlation—0
An Empirical Study of the Effectiveness of Using a Replay Buffer on Mode Discovery in GFlowNets—0
Combining model-predictive control and predictive reinforcement learning for stable quadrupedal robot locomotion—0
Efficient Action Robust Reinforcement Learning with Probabilistic Policy Execution Uncertainty—0
SafeDreamer: Safe Reinforcement Learning with World ModelsCode1
Why Guided Dialog Policy Learning performs well? Understanding the role of adversarial learning and its alternative—0
Robotic Manipulation Datasets for Offline Compositional Reinforcement LearningCode1
PID-Inspired Inductive Biases for Deep Reinforcement Learning in Partially Observable Control TasksCode1
Transformers in Reinforcement Learning: A Survey—0
Learning Decentralized Partially Observable Mean Field Control for Artificial Collective Behavior—0
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Benchmark Results

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