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 4526–4550 of 15113 papers

TitleStatusHype
Reinforcement Learning for Dynamic Memory AllocationCode0
Visualizing and Understanding Atari AgentsCode0
Pontryagin Optimal Control via Neural NetworksCode0
Neural Keyphrase Generation via Reinforcement Learning with Adaptive RewardsCode0
Visual Transfer between Atari Games using Competitive Reinforcement LearningCode0
Visual Transfer for Reinforcement Learning via Wasserstein Domain ConfusionCode0
POPO: Pessimistic Offline Policy OptimizationCode0
ViZDoom: A Doom-based AI Research Platform for Visual Reinforcement LearningCode0
ViZDoom Competitions: Playing Doom from PixelsCode0
Reinforcement Learning for Few-Shot Text Generation AdaptationCode0
On the Design of Safe Continual RL Methods for Control of Nonlinear SystemsCode0
Neural Logic Reinforcement LearningCode0
Selective Token Generation for Few-shot Natural Language GenerationCode0
Neural Lyapunov Function Approximation with Self-Supervised Reinforcement LearningCode0
Metrics and continuity in reinforcement learningCode0
VQC-Based Reinforcement Learning with Data Re-uploading: Performance and TrainabilityCode0
VRKitchen: an Interactive 3D Virtual Environment for Task-oriented LearningCode0
On the Effectiveness of Offline RL for Dialogue Response GenerationCode0
Self-adaptive Torque Vectoring Controller Using Reinforcement LearningCode0
Multi-Agent Image Classification via Reinforcement LearningCode0
Reinforcement Learning for Improving Agent DesignCode0
Vulnerability of Deep Reinforcement Learning to Policy Induction AttacksCode0
Neural Map: Structured Memory for Deep Reinforcement LearningCode0
WALL-E: An Efficient Reinforcement Learning Research FrameworkCode0
Self-Correcting Models for Model-Based Reinforcement LearningCode0
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Benchmark Results

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