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 8326–8350 of 15113 papers

TitleStatusHype
Policy-Based Radiative Transfer: Solving the 2-Level Atom Non-LTE Problem using Soft Actor-Critic Reinforcement Learning—0
Policy-Based Trajectory Clustering in Offline Reinforcement Learning—0
Policy Certificates: Towards Accountable Reinforcement Learning—0
PolicyCleanse: Backdoor Detection and Mitigation for Competitive Reinforcement Learning—0
PolicyClusterGCN: Identifying Efficient Clusters for Training Graph Convolutional Networks—0
Policy Distillation and Value Matching in Multiagent Reinforcement Learning—0
Policy Distillation with Selective Input Gradient Regularization for Efficient Interpretability—0
Policy-Driven World Model Adaptation for Robust Offline Model-based Reinforcement Learning—0
Policy Entropy for Out-of-Distribution Classification—0
Policy Error Bounds for Model-Based Reinforcement Learning with Factored Linear Models—0
Policy Evaluation and Seeking for Multi-Agent Reinforcement Learning via Best Response—0
Policy Finetuning: Bridging Sample-Efficient Offline and Online Reinforcement Learning—0
Policy-focused Agent-based Modeling using RL Behavioral Models—0
Policy Fusion for Adaptive and Customizable Reinforcement Learning Agents—0
Policy Generalization In Capacity-Limited Reinforcement Learning—0
PolicyGNN: Aggregation Optimization for Graph Neural Networks—0
Policy-Gradient Algorithms Have No Guarantees of Convergence in Linear Quadratic Games—0
Policy Gradient Algorithms with Monte Carlo Tree Learning for Non-Markov Decision Processes—0
Policy Gradient based Quantum Approximate Optimization Algorithm—0
Policy Gradient Coagent Networks—0
Policy Gradient For Multidimensional Action Spaces: Action Sampling and Entropy Bonus—0
Policy Gradient for Reinforcement Learning with General Utilities—0
Policy Gradient Method For Robust Reinforcement Learning—0
Policy Gradient Methods for Reinforcement Learning with Function Approximation and Action-Dependent Baselines—0
Policy Gradient Methods for Risk-Sensitive Distributional Reinforcement Learning with Provable Convergence—0
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Benchmark Results

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