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 14851–14900 of 15113 papers

TitleStatusHype
Inverse Reinforcement Learning with Locally Consistent Reward Functions—0
On Learning to Think: Algorithmic Information Theory for Novel Combinations of Reinforcement Learning Controllers and Recurrent Neural World Models—0
Robotic Search & Rescue via Online Multi-task Reinforcement Learning—0
Reinforcement Learning Applied to an Electric Water Heater: From Theory to Practice—0
Multiagent Cooperation and Competition with Deep Reinforcement LearningCode1
On the convergence of cycle detection for navigational reinforcement learning—0
Strategic Dialogue Management via Deep Reinforcement LearningCode0
MazeBase: A Sandbox for Learning from GamesCode0
Dueling Network Architectures for Deep Reinforcement LearningCode0
Conditional Computation in Neural Networks for faster modelsCode0
Policy DistillationCode0
Actor-Mimic: Deep Multitask and Transfer Reinforcement LearningCode0
Active Object Localization with Deep Reinforcement LearningCode0
Prioritized Experience ReplayCode1
Deep Reinforcement Learning with a Natural Language Action SpaceCode0
Deep Reinforcement Learning in Parameterized Action SpaceCode1
Towards Vision-Based Deep Reinforcement Learning for Robotic Motion Control—0
Doubly Robust Off-policy Value Evaluation for Reinforcement Learning—0
Learning Unfair Trading: a Market Manipulation Analysis From the Reinforcement Learning Perspective—0
Generating Text with Deep Reinforcement Learning—0
Sample Complexity of Episodic Fixed-Horizon Reinforcement Learning—0
On the Computability of AIXI—0
Dual Control for Approximate Bayesian Reinforcement Learning—0
Data-Efficient Learning of Feedback Policies from Image Pixels using Deep Dynamical Models—0
Variational Information Maximisation for Intrinsically Motivated Reinforcement LearningCode0
Model-based Reinforcement Learning with Parametrized Physical Models and Optimism-Driven Exploration—0
One-Shot Learning of Manipulation Skills with Online Dynamics Adaptation and Neural Network Priors—0
Deep Reinforcement Learning with Double Q-learningCode1
Learning Deep Control Policies for Autonomous Aerial Vehicles with MPC-Guided Policy Search—0
Deep Spatial Autoencoders for Visuomotor LearningCode0
Benchmarking for Bayesian Reinforcement Learning—0
Optimization of anemia treatment in hemodialysis patients via reinforcement learning—0
Compatible Value Gradients for Reinforcement Learning of Continuous Deep Policies—0
Recurrent Reinforcement Learning: A Hybrid Approach—0
Continuous control with deep reinforcement learningCode1
Reinforcement Learning with Parameterized ActionsCode0
Giraffe: Using Deep Reinforcement Learning to Play ChessCode1
Hyper-parameter Optimisation of Gaussian Process Reinforcement Learning for Statistical Dialogue Management—0
Optimising Turn-Taking Strategies With Reinforcement Learning—0
Reinforcement Learning of Multi-Issue Negotiation Dialogue Policies—0
Reinforcement Learning in Multi-Party Trading Dialog—0
A Cognitive Architecture Based on a Learning Classifier System with Spiking Classifiers—0
Learning Efficient Representations for Reinforcement Learning—0
Multi-agent Reinforcement Learning with Sparse Interactions by Negotiation and Knowledge Transfer—0
Distributed Deep Q-Learning—0
Action-Conditional Video Prediction using Deep Networks in Atari GamesCode0
A Reinforcement Learning Approach to Online Learning of Decision Trees—0
Reinforcement Learning for the Unit Commitment Problem—0
Maximum Entropy Deep Inverse Reinforcement LearningCode0
Massively Parallel Methods for Deep Reinforcement LearningCode0
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Benchmark Results

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