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 7376–7400 of 15113 papers

TitleStatusHype
Making Curiosity Explicit in Vision-based RL—0
Adaptive Informative Path Planning Using Deep Reinforcement Learning for UAV-based Active Sensing—0
Exploratory State Representation LearningCode0
Exploring More When It Needs in Deep Reinforcement Learning—0
Deep Reinforcement Learning with Adjustments—0
A First-Occupancy Representation for Reinforcement Learning—0
Learning of Parameters in Behavior Trees for Movement SkillsCode1
Efficiently Training On-Policy Actor-Critic Networks in Robotic Deep Reinforcement Learning with Demonstration-like Sampled Exploration—0
From internal models toward metacognitive AI—0
DRL-based Slice Placement under Realistic Network Load Conditions—0
MiniHack the Planet: A Sandbox for Open-Ended Reinforcement Learning Research—0
Model-Free Reinforcement Learning for Optimal Control of MarkovDecision Processes Under Signal Temporal Logic Specifications—0
Towards Reinforcement Learning for Pivot-based Neural Machine Translation with Non-autoregressive Transformer—0
On the Feasibility of Learning Finger-gaiting In-hand Manipulation with Intrinsic Sensing—0
MetaDrive: Composing Diverse Driving Scenarios for Generalizable Reinforcement LearningCode2
Prioritized Experience-based Reinforcement Learning with Human Guidance for Autonomous DrivingCode1
Deep Reinforcement Learning for Wireless Scheduling in Distributed Networked Control—0
L^2NAS: Learning to Optimize Neural Architectures via Continuous-Action Reinforcement Learning—0
Stackelberg Actor-Critic: Game-Theoretic Reinforcement Learning AlgorithmsCode1
Emergent behavior and neural dynamics in artificial agents tracking turbulent plumesCode1
Neuroprospecting with DeepRL agents—0
Go-Blend behavior and affect—0
Adaptive Sampling Quasi-Newton Methods for Zeroth-Order Stochastic Optimization—0
Learnable Triangulation for Deep Learning-based 3D Reconstruction of Objects of Arbitrary Topology from Single RGB Images—0
The f-Divergence Reinforcement Learning Framework—0
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Benchmark Results

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