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 8351–8375 of 15113 papers

TitleStatusHype
Exploratory State Representation LearningCode0
Identifying Reasoning Flaws in Planning-Based RL Using Tree Explanations—0
An Offline Deep Reinforcement Learning for Maintenance Decision-Making—0
Exploring More When It Needs in Deep Reinforcement Learning—0
Deep Reinforcement Learning with Adjustments—0
Efficiently Training On-Policy Actor-Critic Networks in Robotic Deep Reinforcement Learning with Demonstration-like Sampled Exploration—0
DRL-based Slice Placement under Realistic Network Load Conditions—0
From internal models toward metacognitive AI—0
Towards Reinforcement Learning for Pivot-based Neural Machine Translation with Non-autoregressive Transformer—0
Model-Free Reinforcement Learning for Optimal Control of MarkovDecision Processes Under Signal Temporal Logic Specifications—0
MiniHack the Planet: A Sandbox for Open-Ended Reinforcement Learning Research—0
On the Feasibility of Learning Finger-gaiting In-hand Manipulation with Intrinsic Sensing—0
Deep Reinforcement Learning for Wireless Scheduling in Distributed Networked Control—0
L^2NAS: Learning to Optimize Neural Architectures via Continuous-Action Reinforcement Learning—0
Adaptive Sampling Quasi-Newton Methods for Zeroth-Order Stochastic Optimization—0
A Graph Policy Network Approach for Volt-Var Control in Power Distribution Systems—0
Go-Blend behavior and affect—0
Learnable Triangulation for Deep Learning-based 3D Reconstruction of Objects of Arbitrary Topology from Single RGB Images—0
Combining Contention-Based Spectrum Access and Adaptive Modulation using Deep Reinforcement Learning—0
The f-Divergence Reinforcement Learning Framework—0
Parameter-free Reduction of the Estimation Bias in Deep Reinforcement Learning for Deterministic Policy GradientsCode0
Regularization Guarantees Generalization in Bayesian Reinforcement Learning through Algorithmic Stability—0
Neuroprospecting with DeepRL agents—0
PredictionNet: Real-Time Joint Probabilistic Traffic Prediction for Planning, Control, and Simulation—0
Reinforcement Learning Under Algorithmic Triage—0
Show:102550
← PrevPage 335 of 605Next →

Benchmark Results

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