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 9701–9725 of 15113 papers

TitleStatusHype
Partial Off-Policy Learning: Balance Accuracy and Diversity for Human-Oriented Image Captioning—0
Self-Supervised Continuous Control without Policy Gradient—0
Robust Multi-Agent Reinforcement Learning Driven by Correlated Equilibrium—0
Re-examining Routing Networks for Multi-task Learning—0
Scalable Bayesian Inverse Reinforcement Learning by Auto-Encoding Reward—0
PAC-Bayesian Randomized Value Function with Informative Prior—0
MQES: Max-Q Entropy Search for Efficient Exploration in Continuous Reinforcement Learning—0
On Trade-offs of Image Prediction in Visual Model-Based Reinforcement Learning—0
Weighted Bellman Backups for Improved Signal-to-Noise in Q-Updates—0
Structure and randomness in planning and reinforcement learningCode0
Understanding and Leveraging Causal Relations in Deep Reinforcement Learning—0
Visual Imitation with Reinforcement Learning using Recurrent Siamese Networks—0
RECONNAISSANCE FOR REINFORCEMENT LEARNING WITH SAFETY CONSTRAINTS—0
When Is Generalizable Reinforcement Learning Tractable?—0
R-LAtte: Attention Module for Visual Control via Reinforcement Learning—0
What are the Statistical Limits of Batch RL with Linear Function Approximation?—0
Regioned Episodic Reinforcement Learning—0
Fine-Tuning Offline Reinforcement Learning with Model-Based Policy Optimization—0
Aspect-based Sentiment Classification via Reinforcement Learning—0
A Simple Sparse Denoising Layer for Robust Deep Learning—0
Learning Latent Landmarks for Generalizable Planning—0
Coordinated Multi-Agent Exploration Using Shared Goals—0
FSV: Learning to Factorize Soft Value Function for Cooperative Multi-Agent Reinforcement Learning—0
Cross-State Self-Constraint for Feature Generalization in Deep Reinforcement Learning—0
Learning Predictive Communication by Imagination in Networked System Control—0
Show:102550
← PrevPage 389 of 605Next →

Benchmark Results

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