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 13851–13900 of 15113 papers

TitleStatusHype
Deep Communicating Agents for Abstractive Summarization—0
World ModelsCode1
Scalable photonic reinforcement learning by time-division multiplexing of laser chaos—0
Autonomous Ramp Merge Maneuver Based on Reinforcement Learning with Continuous Action Space—0
The Importance of Constraint Smoothness for Parameter Estimation in Computational Cognitive Modeling—0
Accelerating Learning in Constructive Predictive Frameworks with the Successor Representation—0
Deep Reinforcement Learning with Model Learning and Monte Carlo Tree Search in Minecraft—0
DOP: Deep Optimistic Planning with Approximate Value Function Evaluation—0
Learning State Representations for Query Optimization with Deep Reinforcement Learning—0
Neuronal Circuit PoliciesCode0
Look Before You Leap: Bridging Model-Free and Model-Based Reinforcement Learning for Planned-Ahead Vision-and-Language NavigationCode0
End-to-End Video Captioning with Multitask Reinforcement LearningCode0
Learning Robotic Assembly from CAD—0
Variance Reduction for Policy Gradient with Action-Dependent Factorized Baselines—0
Natural Gradient Deep Q-learning—0
Meta Reinforcement Learning with Latent Variable Gaussian Processes—0
Optimizing Sponsored Search Ranking Strategy by Deep Reinforcement Learning—0
Composable Deep Reinforcement Learning for Robotic ManipulationCode0
Simple random search provides a competitive approach to reinforcement learningCode1
Setting up a Reinforcement Learning Task with a Real-World RobotCode0
Automated Curriculum Learning by Rewarding Temporally Rare EventsCode0
Rearrangement with Nonprehensile Manipulation Using Deep Reinforcement Learning—0
Neural Text Generation: Past, Present and Beyond—0
Measurement-based adaptation protocol with quantum reinforcement learning—0
Automated Speed and Lane Change Decision Making using Deep Reinforcement Learning—0
Imitation Learning with Concurrent Actions in 3D Games—0
Hierarchical Reinforcement Learning: Approximating Optimal Discounted TSP Using Local Policies—0
Learning to Explore with Meta-Policy Gradient—0
Active Reinforcement Learning with Monte-Carlo Tree Search—0
Policy Search in Continuous Action Domains: an Overview—0
Soft-Robust Actor-Critic Policy-Gradient—0
Deep reinforcement learning for time series: playing idealized trading gamesCode0
Kickstarting Deep Reinforcement Learning—0
Variance Networks: When Expectation Does Not Meet Your ExpectationsCode0
SA-IGA: A Multiagent Reinforcement Learning Method Towards Socially Optimal Outcomes—0
Feudal Reinforcement Learning for Dialogue Management in Large Domains—0
A Multi-Objective Deep Reinforcement Learning Framework—0
DeepCAS: A Deep Reinforcement Learning Algorithm for Control-Aware Scheduling—0
A Brandom-ian view of Reinforcement Learning towards strong-AI—0
Accelerated Methods for Deep Reinforcement LearningCode2
Extracting Action Sequences from Texts Based on Deep Reinforcement Learning—0
Intent-aware Multi-agent Reinforcement Learning—0
Personalized Exposure Control Using Adaptive Metering and Reinforcement Learning—0
Synthesizing Neural Network Controllers with Probabilistic Model based Reinforcement LearningCode0
Smoothed Action Value Functions for Learning Gaussian Policies—0
Variance-Aware Regret Bounds for Undiscounted Reinforcement Learning in MDPs—0
OIL: Observational Imitation Learning—0
Some Considerations on Learning to Explore via Meta-Reinforcement LearningCode0
Model-Free Control for Distributed Stream Data Processing using Deep Reinforcement Learning—0
Distributed Prioritized Experience ReplayCode3
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Benchmark Results

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