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 14051–14100 of 15113 papers

TitleStatusHype
Deep Reinforcement Learning for Programming Language CorrectionCode0
Pretraining Deep Actor-Critic Reinforcement Learning Algorithms With Expert Demonstrations—0
Deep Reinforcement Learning using Capsules in Advanced Game Environments—0
Barrier-Certified Adaptive Reinforcement Learning with Applications to Brushbot Navigation—0
Learning the Reward Function for a Misspecified ModelCode0
Deep Reinforcement Learning for Dynamic Treatment Regimes on Medical Registry Data—0
FlashRL: A Reinforcement Learning Platform for Flash Games—0
Directly Estimating the Variance of the λ-Return Using Temporal-Difference Methods—0
Psychlab: A Psychology Laboratory for Deep Reinforcement Learning AgentsCode0
Analyzing Language Learned by an Active Question Answering Agent—0
Curiosity-driven reinforcement learning with homeostatic regulation—0
Cross-Domain Transfer in Reinforcement Learning using Target Apprentice—0
A Deep Reinforcement Learning Chatbot (Short Version)—0
Learning model-based strategies in simple environments with hierarchical q-networksCode0
Deep Dyna-Q: Integrating Planning for Task-Completion Dialogue Policy LearningCode0
Experience-driven Networking: A Deep Reinforcement Learning based Approach—0
Reinforcement Learning based Recommender System using Biclustering Technique—0
The QLBS Q-Learner Goes NuQLear: Fitted Q Iteration, Inverse RL, and Option Portfolios—0
The Case for Automatic Database Administration using Deep Reinforcement Learning—0
Cellular-Connected UAVs over 5G: Deep Reinforcement Learning for Interference Management—0
GitGraph - Architecture Search Space Creation through Frequent Computational Subgraph Mining—0
Cooperative Multi-Agent Reinforcement Learning for Low-Level Wireless Communication—0
Deep Reinforcement Fuzzing—0
Deep Reinforcement Learning of Cell Movement in the Early Stage of C. elegans Embryogenesis—0
Autonomous Driving in Reality with Reinforcement Learning and Image Translation—0
Expected Policy Gradients for Reinforcement Learning—0
DeepTraffic: Crowdsourced Hyperparameter Tuning of Deep Reinforcement Learning Systems for Multi-Agent Dense Traffic NavigationCode0
Distributed Deep Reinforcement Learning: Learn how to play Atari games in 21 minutesCode0
Competitive Multi-agent Inverse Reinforcement Learning with Sub-optimal DemonstrationsCode0
Trading the Twitter Sentiment with Reinforcement Learning—0
Sample-Efficient Reinforcement Learning through Transfer and Architectural Priors—0
Using reinforcement learning to learn how to play text-based gamesCode0
Faster Deep Q-learning using Neural Episodic Control—0
Jointly Learning to Construct and Control Agents using Deep Reinforcement LearningCode0
Deep Reinforcement Learning based Optimal Control of Hot Water Systems—0
Long Term Memory Network for Combinatorial Optimization Problems—0
Learning Gaussian Policies from Smoothed Action Value Functions—0
Action-dependent Control Variates for Policy Optimization via Stein Identity—0
Learning objects from pixels—0
LatentPoison -- Adversarial Attacks On The Latent Space—0
Faster Reinforcement Learning with Expert State Sequences—0
A Hierarchical Model for Device Placement—0
AUTOMATA GUIDED HIERARCHICAL REINFORCEMENT LEARNING FOR ZERO-SHOT SKILL COMPOSITION—0
Domain Adaptation for Deep Reinforcement Learning in Visually Distinct Games—0
Latent forward model for Real-time Strategy game planning with incomplete information—0
Alpha-divergence bridges maximum likelihood and reinforcement learning in neural sequence generation—0
A dynamic game approach to training robust deep policies—0
Do Deep Reinforcement Learning Algorithms really Learn to Navigate?—0
Exploring Deep Recurrent Models with Reinforcement Learning for Molecule Design—0
Learning Robust Rewards with Adverserial Inverse Reinforcement Learning—0
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Benchmark Results

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