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 4626–4650 of 15113 papers

TitleStatusHype
An approach to implement Reinforcement Learning for Heterogeneous Vehicular Networks—0
An Approach to Partial Observability in Games: Learning to Both Act and Observe—0
A differential Hebbian framework for biologically-plausible motor control—0
An Architecture for Deploying Reinforcement Learning in Industrial Environments—0
An Attempt to Model Human Trust with Reinforcement Learning—0
A Natural Actor-Critic Algorithm with Downside Risk Constraints—0
A Natural Extension To Online Algorithms For Hybrid RL With Limited Coverage—0
An Auction-based Marketplace for Model Trading in Federated Learning—0
An Augmented Reality Platform for Introducing Reinforcement Learning to K-12 Students with Robots—0
An Automated Portfolio Trading System with Feature Preprocessing and Recurrent Reinforcement Learning—0
An Automated Reinforcement Learning Reward Design Framework with Large Language Model for Cooperative Platoon Coordination—0
An Autonomous Free Airspace En-route Controller using Deep Reinforcement Learning Techniques—0
An Autonomous Network Orchestration Framework Integrating Large Language Models with Continual Reinforcement Learning—0
Ancestral Reinforcement Learning: Unifying Zeroth-Order Optimization and Genetic Algorithms for Reinforcement Learning—0
Anderson Acceleration for Reinforcement Learning—0
A Nearly Optimal and Low-Switching Algorithm for Reinforcement Learning with General Function Approximation—0
A Near-Optimal Algorithm for Safe Reinforcement Learning Under Instantaneous Hard Constraints—0
An Efficient Dynamic Sampling Policy For Monte Carlo Tree Search—0
Efficient Training of Generalizable Visuomotor Policies via Control-Aware Augmentation—0
An Efficient, Generalized Bellman Update For Cooperative Inverse Reinforcement Learning—0
An Elementary Proof that Q-learning Converges Almost Surely—0
An Empirical Analysis of Multiple-Turn Reasoning Strategies in Reading Comprehension Tasks—0
An Empirical Comparison of Neural Architectures for Reinforcement Learning in Partially Observable Environments—0
An Empirical Study of the Effectiveness of Using a Replay Buffer on Mode Discovery in GFlowNets—0
An Empowerment-based Solution to Robotic Manipulation Tasks with Sparse Rewards—0
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Benchmark Results

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