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 14401–14450 of 15113 papers

TitleStatusHype
ELF: An Extensive, Lightweight and Flexible Research Platform for Real-time Strategy GamesCode0
Maintaining cooperation in complex social dilemmas using deep reinforcement learning—0
OPEB: Open Physical Environment Benchmark for Artificial Intelligence—0
Efficient Probabilistic Performance Bounds for Inverse Reinforcement LearningCode0
Hashing over Predicted Future Frames for Informed Exploration of Deep Reinforcement Learning—0
Grammatical Error Correction with Neural Reinforcement Learning—0
Action-Decision Networks for Visual Tracking With Deep Reinforcement LearningCode0
Sample-efficient Actor-Critic Reinforcement Learning with Supervised Data for Dialogue Management—0
Neural Sequence Model Training via α-divergence MinimizationCode0
Noisy Networks for ExplorationCode0
Neural SLAM: Learning to Explore with External MemoryCode0
Path Integral Networks: End-to-End Differentiable Optimal Control—0
Actor-Critic Sequence Training for Image Captioning—0
Learning to Learn: Meta-Critic Networks for Sample Efficient Learning—0
Interpretability via Model Extraction—0
Uncertainty Decomposition in Bayesian Neural Networks with Latent Variables—0
Count-Based Exploration in Feature Space for Reinforcement LearningCode0
Temporal-related Convolutional-Restricted-Boltzmann-Machine capable of learning relational order via reinforcement learning procedure?—0
A Self-Adaptive Proposal Model for Temporal Action Detection based on Reinforcement LearningCode0
Structure Learning in Motor Control:A Deep Reinforcement Learning Model—0
Observational Learning by Reinforcement Learning—0
Toward Real-Time Decentralized Reinforcement Learning using Finite Support Basis Functions—0
Policy Gradient Methods for Reinforcement Learning with Function Approximation and Action-Dependent Baselines—0
Data-Efficient Reinforcement Learning with Probabilistic Model Predictive ControlCode0
Dex: Incremental Learning for Complex Environments in Deep Reinforcement LearningCode0
Pedestrian Prediction by Planning using Deep Neural Networks—0
Sub-domain Modelling for Dialogue Management with Hierarchical Reinforcement Learning—0
Reinforcement Learning under Model Mismatch—0
Zero-Shot Task Generalization with Multi-Task Deep Reinforcement LearningCode0
Deep learning-based numerical methods for high-dimensional parabolic partial differential equations and backward stochastic differential equationsCode0
Reinforcement Learning with Budget-Constrained Nonparametric Function Approximation for Opportunistic Spectrum Access—0
On Optimistic versus Randomized Exploration in Reinforcement Learning—0
Hybrid Reward Architecture for Reinforcement LearningCode0
Device Placement Optimization with Reinforcement LearningCode0
Deep reinforcement learning from human preferencesCode0
ACCNet: Actor-Coordinator-Critic Net for "Learning-to-Communicate" with Deep Multi-agent Reinforcement Learning—0
Symmetry Learning for Function Approximation in Reinforcement Learning—0
Unlocking the Potential of Simulators: Design with RL in Mind—0
Efficient Reinforcement Learning via Initial Pure Exploration—0
Parameter Space Noise for ExplorationCode0
Towards Synthesizing Complex Programs from Input-Output Examples—0
UCB Exploration via Q-Ensembles—0
A method for the online construction of the set of states of a Markov Decision Process using Answer Set Programming—0
Actor-Critic for Linearly-Solvable Continuous MDP with Partially Known Dynamics—0
Interpolated Policy Gradient: Merging On-Policy and Off-Policy Gradient Estimation for Deep Reinforcement Learning—0
Reinforcement Learning for Learning Rate Control—0
The Atari Grand Challenge DatasetCode0
Sequential Dynamic Decision Making with Deep Neural Nets on a Test-Time Budget—0
Objective-Reinforced Generative Adversarial Networks (ORGAN) for Sequence Generation ModelsCode0
Universal Reinforcement Learning Algorithms: Survey and ExperimentsCode0
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Benchmark Results

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