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 7151–7175 of 15113 papers

TitleStatusHype
Explainable Deep Reinforcement Learning: State of the Art and Challenges—0
Explainable Deep Reinforcement Learning Using Introspection in a Non-episodic Task—0
Explainable Reinforcement Learning: A Survey—0
Explainable Reinforcement Learning for Broad-XAI: A Conceptual Framework and Survey—0
Explainable Reinforcement Learning on Financial Stock Trading using SHAP—0
Explainable Reinforcement Learning Through Goal-Based Explanations—0
Explainable Reinforcement Learning via Temporal Policy Decomposition—0
Explainable robotic systems: Understanding goal-driven actions in a reinforcement learning scenario—0
Explaining a Deep Reinforcement Learning Docking Agent Using Linear Model Trees with User Adapted Visualization—0
Explaining Agent's Decision-making in a Hierarchical Reinforcement Learning Scenario—0
Explaining Conditions for Reinforcement Learning Behaviors from Real and Imagined Data—0
Explaining Deep Reinforcement Learning Agents In The Atari Domain through a Surrogate Model—0
Explaining Online Reinforcement Learning Decisions of Self-Adaptive Systems—0
Explaining Reinforcement Learning to Mere Mortals: An Empirical Study—0
Explanation Augmented Feedback in Human-in-the-Loop Reinforcement Learning—0
Explanation of Reinforcement Learning Model in Dynamic Multi-Agent System—0
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
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Benchmark Results

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