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 4576–4600 of 15113 papers

TitleStatusHype
A Multifidelity Sim-to-Real Pipeline for Verifiable and Compositional Reinforcement Learning—0
A Multimodal Learning-based Approach for Autonomous Landing of UAV—0
A MultiModal Social Robot Toward Personalized Emotion Interaction—0
A Multi-Objective Deep Reinforcement Learning Framework—0
An Abstraction-based Method to Check Multi-Agent Deep Reinforcement-Learning Behaviors—0
An Actor-Critic-Attention Mechanism for Deep Reinforcement Learning in Multi-view Environments—0
An Actor-Critic Method for Simulation-Based Optimization—0
An A* Curriculum Approach to Reinforcement Learning for RGBD Indoor Robot Navigation—0
An Adaptable Approach to Learn Realistic Legged Locomotion without Examples—0
An Adaptive Multi-Agent Physical Layer Security Framework for Cognitive Cyber-Physical Systems—0
An adaptive synchronization approach for weights of deep reinforcement learning—0
An advantage based policy transfer algorithm for reinforcement learning with measures of transferability—0
An Affective Robot Companion for Assisting the Elderly in a Cognitive Game Scenario—0
An agent-driven semantical identifier using radial basis neural networks and reinforcement learning—0
I Cast Detect Thoughts: Learning to Converse and Guide with Intents and Theory-of-Mind in Dungeons and Dragons—0
An Algorithmic Theory of Metacognition in Minds and Machines—0
Analog Circuit Design with Dyna-Style Reinforcement Learning—0
An Alternative to Variance: Gini Deviation for Risk-averse Policy Gradient—0
Analysing Congestion Problems in Multi-agent Reinforcement Learning—0
Analysing Deep Reinforcement Learning Agents Trained with Domain Randomisation—0
Analysis and Improvement of Policy Gradient Estimation—0
Analysis of Agent Expertise in Ms. Pac-Man using Value-of-Information-based Policies—0
Analysis of Evolutionary Behavior in Self-Learning Media Search Engines—0
Analysis of Information Propagation in Ethereum Network Using Combined Graph Attention Network and Reinforcement Learning to Optimize Network Efficiency and Scalability—0
Analysis of Randomization Effects on Sim2Real Transfer in Reinforcement Learning for Robotic Manipulation Tasks—0
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Benchmark Results

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