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 14651–14700 of 15113 papers

TitleStatusHype
Towards deep learning with spiking neurons in energy based models with contrastive Hebbian plasticity—0
Stochastic Primal-Dual Methods and Sample Complexity of Reinforcement Learning—0
Towards Information-Seeking Agents—0
Hierarchy through Composition with Linearly Solvable Markov Decision Processes—0
Learning to superoptimize programs - Workshop Version—0
Deep Learning of Robotic Tasks without a Simulator using Strong and Weak Human Supervision—0
Bayesian Optimization with Robust Bayesian Neural NetworksCode0
Bootstrapping incremental dialogue systems: using linguistic knowledge to learn from minimal data—0
Generalizing Skills with Semi-Supervised Reinforcement Learning—0
Adaptive optimal training of animal behavior—0
Linear Feature Encoding for Reinforcement Learning—0
Playing Doom with SLAM-Augmented Deep Reinforcement LearningCode0
Showing versus doing: Teaching by demonstration—0
Exploration for Multi-task Reinforcement Learning with Deep Generative Models—0
Learning to Compose Words into Sentences with Reinforcement Learning—0
Improving Policy Gradient by Exploring Under-appreciated Rewards—0
Nonparametric General Reinforcement Learning—0
Training an Interactive Humanoid Robot Using Multimodal Deep Reinforcement LearningCode0
Deep Reinforcement Learning for Multi-Domain Dialogue SystemsCode0
A Simple, Fast Diverse Decoding Algorithm for Neural GenerationCode0
Multiscale Inverse Reinforcement Learning using Diffusion Wavelets—0
Recurrent Attention Models for Depth-Based Person Identification—0
Variational Intrinsic ControlCode0
Memory Lens: How Much Memory Does an Agent Use?—0
Options Discovery with Budgeted Reinforcement Learning—0
A Deep Learning Approach for Joint Video Frame and Reward Prediction in Atari Games—0
Reinforcement Learning through Asynchronous Advantage Actor-Critic on a GPUCode0
Learning to reinforcement learnCode0
Reinforcement Learning with Unsupervised Auxiliary TasksCode0
Reinforcement Learning in Rich-Observation MDPs using Spectral Methods—0
Hierarchical Object Detection with Deep Reinforcement LearningCode0
A Connection between Generative Adversarial Networks, Inverse Reinforcement Learning, and Energy-Based ModelsCode0
Learning to Navigate in Complex EnvironmentsCode0
Fairness in Reinforcement Learning—0
Sequence Tutor: Conservative Fine-Tuning of Sequence Generation Models with KL-control—0
RL^2: Fast Reinforcement Learning via Slow Reinforcement LearningCode0
Reinforcement Learning Approach for Parallelization in Filters Aggregation Based Feature Selection Algorithms—0
Q-Prop: Sample-Efficient Policy Gradient with An Off-Policy CriticCode0
Designing Neural Network Architectures using Reinforcement LearningCode0
Averaged-DQN: Variance Reduction and Stabilization for Deep Reinforcement Learning—0
Learning to Perform Physics Experiments via Deep Reinforcement Learning—0
Modular Multitask Reinforcement Learning with Policy SketchesCode0
Neural Architecture Search with Reinforcement LearningCode0
Learning to Play in a Day: Faster Deep Reinforcement Learning by Optimality TighteningCode0
Multi-task learning with deep model based reinforcement learning—0
Quantile Reinforcement Learning—0
Using a Deep Reinforcement Learning Agent for Traffic Signal Control—0
Learning Locomotion Skills Using DeepRL: Does the Choice of Action Space Matter?—0
Combating Reinforcement Learning's Sisyphean Curse with Intrinsic Fear—0
Learning Runtime Parameters in Computer Systems with Delayed Experience Injection—0
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Benchmark Results

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