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 2376–2400 of 15113 papers

TitleStatusHype
Learning from Demonstration without DemonstrationsCode0
Learning Dynamic Context Augmentation for Global Entity LinkingCode0
Learning-Driven Exploration for Reinforcement LearningCode0
Learning Fair Policies in Multiobjective (Deep) Reinforcement Learning with Average and Discounted RewardsCode0
Combining imitation and deep reinforcement learning to accomplish human-level performance on a virtual foraging taskCode0
BAIL: Best-Action Imitation Learning for Batch Deep Reinforcement LearningCode0
Adaptive ROI Generation for Video Object Segmentation Using Reinforcement LearningCode0
Understanding the Effects of Second-Order Approximations in Natural Policy Gradient Reinforcement LearningCode0
A Multi-Agent Reinforcement Learning Framework for Off-Policy Evaluation in Two-sided MarketsCode0
Learning Explicit Credit Assignment for Cooperative Multi-Agent Reinforcement Learning via Polarization Policy GradientCode0
Learning Curriculum Policies for Reinforcement LearningCode0
BadRL: Sparse Targeted Backdoor Attack Against Reinforcement LearningCode0
A Multi-Agent Off-Policy Actor-Critic Algorithm for Distributed Reinforcement LearningCode0
Learning Conformal Abstention Policies for Adaptive Risk Management in Large Language and Vision-Language ModelsCode0
Learning Complex Teamwork Tasks Using a Given Sub-task DecompositionCode0
Learning data augmentation policies using augmented random searchCode0
Learning from Learners: Adapting Reinforcement Learning Agents to be Competitive in a Card GameCode0
Baconian: A Unified Open-source Framework for Model-Based Reinforcement LearningCode0
Back to Basics: Deep Reinforcement Learning in Traffic Signal ControlCode0
Learning Bellman Complete Representations for Offline Policy EvaluationCode0
Back to Basics: Benchmarking Canonical Evolution Strategies for Playing AtariCode0
Adaptive Risk-Aware Bidding with Budget Constraint in Display AdvertisingCode0
Amplifying the Imitation Effect for Reinforcement Learning of UCAV's Mission ExecutionCode0
Using a Logarithmic Mapping to Enable Lower Discount Factors in Reinforcement LearningCode0
Learning-based Model Predictive Control for Safe Exploration and Reinforcement LearningCode0
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Benchmark Results

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