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

Showing 71517200 of 15113 papers

TitleStatusHype
Explainable Deep Reinforcement Learning: State of the Art and Challenges0
Explainable Deep Reinforcement Learning Using Introspection in a Non-episodic Task0
Explainable Reinforcement Learning: A Survey0
Explainable Reinforcement Learning for Broad-XAI: A Conceptual Framework and Survey0
Explainable Reinforcement Learning on Financial Stock Trading using SHAP0
Explainable Reinforcement Learning Through Goal-Based Explanations0
Explainable Reinforcement Learning via Temporal Policy Decomposition0
Explainable robotic systems: Understanding goal-driven actions in a reinforcement learning scenario0
Explaining a Deep Reinforcement Learning Docking Agent Using Linear Model Trees with User Adapted Visualization0
Explaining Agent's Decision-making in a Hierarchical Reinforcement Learning Scenario0
Explaining Conditions for Reinforcement Learning Behaviors from Real and Imagined Data0
Explaining Deep Reinforcement Learning Agents In The Atari Domain through a Surrogate Model0
Explaining Online Reinforcement Learning Decisions of Self-Adaptive Systems0
Explaining Reinforcement Learning to Mere Mortals: An Empirical Study0
Explanation Augmented Feedback in Human-in-the-Loop Reinforcement Learning0
Explanation of Reinforcement Learning Model in Dynamic Multi-Agent System0
Explicit Explore, Exploit, or Escape (E^4): near-optimal safety-constrained reinforcement learning in polynomial time0
Explicit Lipschitz Value Estimation Enhances Policy Robustness Against Perturbation0
Explicit Mean-Square Error Bounds for Monte-Carlo and Linear Stochastic Approximation0
Explicit Pareto Front Optimization for Constrained Reinforcement Learning0
Explicit Planning for Efficient Exploration in Reinforcement Learning0
Explicit Recall for Efficient Exploration0
Explicit User Manipulation in Reinforcement Learning Based Recommender Systems0
Exploiting Action Impact Regularity and Exogenous State Variables for Offline Reinforcement Learning0
Exploiting Contextual Structure to Generate Useful Auxiliary Tasks0
Exploiting Deep Reinforcement Learning for Edge Caching in Cell-Free Massive MIMO Systems0
Exploiting Environmental Variation to Improve Policy Robustness in Reinforcement Learning0
Exploiting Estimation Bias in Clipped Double Q-Learning for Continous Control Reinforcement Learning Tasks0
Exploiting generalisation symmetries in accuracy-based learning classifier systems: An initial study0
Exploiting Generalization in Offline Reinforcement Learning via Unseen State Augmentations0
Exploiting generalization in the subspaces for faster model-based learning0
Exploiting Hierarchy for Learning and Transfer in KL-regularized RL0
Facilitating Sim-to-real by Intrinsic Stochasticity of Real-Time Simulation in Reinforcement Learning for Robot Manipulation0
Exploiting Language Instructions for Interpretable and Compositional Reinforcement Learning0
Exploiting Noisy Data in Distant Supervision Relation Classification0
Exploiting Semantic Epsilon Greedy Exploration Strategy in Multi-Agent Reinforcement Learning0
Exploiting Symbolic Heuristics for the Synthesis of Domain-Specific Temporal Planning Guidance using Reinforcement Learning0
Exploiting the potential of deep reinforcement learning for classification tasks in high-dimensional and unstructured data0
Exploiting Unlabeled Data for Feedback Efficient Human Preference based Reinforcement Learning0
Exploration and Incentives in Reinforcement Learning0
Exploration by Distributional Reinforcement Learning0
Exploration by Maximizing Rényi Entropy for Reward-Free RL Framework0
Exploration by Random Network Distillation0
Exploration by Random Reward Perturbation0
Exploration by Uncertainty in Reward Space0
Exploration-Driven Representation Learning in Reinforcement Learning0
Exploration--Exploitation in MDPs with Options0
Exploration-exploitation trade-off for continuous-time episodic reinforcement learning with linear-convex models0
Exploration for Multi-task Reinforcement Learning with Deep Generative Models0
Exploration in Deep Reinforcement Learning: From Single-Agent to Multiagent Domain0
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Benchmark Results

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