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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 9976–10000 of 15113 papers

TitleStatusHype
Disentangling Controllable and Uncontrollable Factors of Variation by Interacting with the World—0
Disentangling Controllable Object through Video Prediction Improves Visual Reinforcement Learning—0
Disentangling Dynamics and Returns: Value Function Decomposition with Future Prediction—0
Disentangling Epistemic and Aleatoric Uncertainty in Reinforcement Learning—0
Disentangling Generalization in Reinforcement Learning—0
Disentangling Options with Hellinger Distance Regularizer—0
Disentangling Recognition and Decision Regrets in Image-Based Reinforcement Learning—0
Disentangling Sources of Risk for Distributional Multi-Agent Reinforcement Learning—0
Disentangling Transfer in Continual Reinforcement Learning—0
DISPATCH: Design Space Exploration of Cyber-Physical Systems—0
Dissipative residual layers for unsupervised implicit parameterization of data manifolds—0
Distal Explanations for Model-free Explainable Reinforcement Learning—0
Distill2Explain: Differentiable decision trees for explainable reinforcement learning in energy application controllers—0
Distillation Strategies for Proximal Policy Optimization—0
Distilled Agent DQN for Provable Adversarial Robustness—0
Distilled Domain Randomization—0
Distilling a Hierarchical Policy for Planning and Control via Representation and Reinforcement Learning—0
Distilling Deep RL Models Into Interpretable Neuro-Fuzzy Systems—0
Distilling Neuron Spike with High Temperature in Reinforcement Learning Agents—0
Distilling Reinforcement Learning Policies for Interpretable Robot Locomotion: Gradient Boosting Machines and Symbolic Regression—0
Distilling the Implicit Multi-Branch Structure in LLMs' Reasoning via Reinforcement Learning—0
Distill Not Only Data but Also Rewards: Can Smaller Language Models Surpass Larger Ones?—0
DisTop: Discovering a Topological representation to learn diverse and rewarding skills—0
Distort-and-Recover: Color Enhancement using Deep Reinforcement Learning—0
Distral: Robust Multitask Reinforcement Learning—0
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Benchmark Results

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