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 10651–10675 of 15113 papers

TitleStatusHype
Explicit Explore, Exploit, or Escape (E^4): near-optimal safety-constrained reinforcement learning in polynomial time—0
Explicit Lipschitz Value Estimation Enhances Policy Robustness Against Perturbation—0
Explicit Mean-Square Error Bounds for Monte-Carlo and Linear Stochastic Approximation—0
Explicit Pareto Front Optimization for Constrained Reinforcement Learning—0
Explicit Planning for Efficient Exploration in Reinforcement Learning—0
Explicit Recall for Efficient Exploration—0
Explicit User Manipulation in Reinforcement Learning Based Recommender Systems—0
Exploiting Action Impact Regularity and Exogenous State Variables for Offline Reinforcement Learning—0
Exploiting Contextual Structure to Generate Useful Auxiliary Tasks—0
Exploiting Deep Reinforcement Learning for Edge Caching in Cell-Free Massive MIMO Systems—0
Exploiting Environmental Variation to Improve Policy Robustness in Reinforcement Learning—0
Exploiting Estimation Bias in Clipped Double Q-Learning for Continous Control Reinforcement Learning Tasks—0
Exploiting generalisation symmetries in accuracy-based learning classifier systems: An initial study—0
Exploiting Generalization in Offline Reinforcement Learning via Unseen State Augmentations—0
Exploiting generalization in the subspaces for faster model-based learning—0
Exploiting Hierarchy for Learning and Transfer in KL-regularized RL—0
Facilitating Sim-to-real by Intrinsic Stochasticity of Real-Time Simulation in Reinforcement Learning for Robot Manipulation—0
Exploiting Language Instructions for Interpretable and Compositional Reinforcement Learning—0
Exploiting Noisy Data in Distant Supervision Relation Classification—0
Exploiting Semantic Epsilon Greedy Exploration Strategy in Multi-Agent Reinforcement Learning—0
Exploiting Symbolic Heuristics for the Synthesis of Domain-Specific Temporal Planning Guidance using Reinforcement Learning—0
Exploiting the potential of deep reinforcement learning for classification tasks in high-dimensional and unstructured data—0
Exploiting Unlabeled Data for Feedback Efficient Human Preference based Reinforcement Learning—0
Exploration and Incentives in Reinforcement Learning—0
Exploration by Distributional Reinforcement Learning—0
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Benchmark Results

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