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 9301–9350 of 15113 papers

TitleStatusHype
Simulation Studies on Deep Reinforcement Learning for Building Control with Human Interaction—0
Metalearning Using Structure-rich Pipeline Representations for Better AutoML—0
RL-Controller: a reinforcement learning framework for active structural control—0
Hybrid computer approach to train a machine learning system—0
A Survey of Forex and Stock Price Prediction Using Deep Learning—0
Constrained Text Generation with Global Guidance -- Case Study on CommonGen—0
Analyzing the Hidden Activations of Deep Policy Networks: Why Representation Matters—0
Adapting User Interfaces with Model-based Reinforcement Learning—0
A Vision Based Deep Reinforcement Learning Algorithm for UAV Obstacle Avoidance—0
Adversarial attacks in consensus-based multi-agent reinforcement learning—0
A Quadratic Actor Network for Model-Free Reinforcement LearningCode0
Auto-COP: Adaptation Generation in Context-Oriented Programming using Reinforcement Learning Options—0
A Reinforcement Learning Based Approach to Play Calling in Football—0
Multi-Task Federated Reinforcement Learning with Adversaries—0
Sample Complexity of Offline Reinforcement Learning with Deep ReLU Networks—0
Robust High-speed Running for Quadruped Robots via Deep Reinforcement Learning—0
Symbolic Reinforcement Learning for Safe RAN Control—0
Policy Search with Rare Significant Events: Choosing the Right Partner to Cooperate withCode0
Streaming Linear System Identification with Reverse Experience Replay—0
Using Cognitive Models to Train Warm Start Reinforcement Learning Agents for Human-Computer Interactions—0
Maximum Entropy RL (Provably) Solves Some Robust RL Problems—0
S4RL: Surprisingly Simple Self-Supervision for Offline Reinforcement Learning—0
Multi-Objective Reinforcement Learning based Multi-Microgrid System Optimisation Problem—0
Improving Context-Based Meta-Reinforcement Learning with Self-Supervised Trajectory Contrastive Learning—0
A Two-stage Framework and Reinforcement Learning-based Optimization Algorithms for Complex Scheduling Problems—0
An Information-Theoretic Perspective on Credit Assignment in Reinforcement Learning—0
Full Gradient DQN Reinforcement Learning: A Provably Convergent Scheme—0
WFA-IRL: Inverse Reinforcement Learning of Autonomous Behaviors Encoded as Weighted Finite Automata—0
Learning to Infer Unseen Contexts in Causal Contextual Reinforcement Learning—0
Automatic Goal Generation using Dynamical Distance Learning—0
A Learning-Based Computational Impact Time Guidance—0
Learning to Explore a Class of Multiple Reward-Free Environments—0
Learning Task Informed Abstractions—0
I am Robot: Neuromuscular Reinforcement Learning to Actuate Human Limbs through Functional Electrical Stimulation—0
Decentralized Circle Formation Control for Fish-like Robots in the Real-world via Reinforcement Learning—0
Challenges for Reinforcement Learning in Healthcare—0
Less Suboptimal Learning and Control in Variational POMDPs—0
Learning State Representations via Temporal Cycle-Consistency Constraint in Model-Based Reinforcement Learning—0
LOCO: Adaptive exploration in reinforcement learning via local estimation of contraction coefficients—0
A Scavenger Hunt for Service RobotsCode0
PsiPhi-Learning: Reinforcement Learning with Demonstrations using Successor Features and Inverse Temporal Difference Learning—0
Pretraining Reward-Free Representations for Data-Efficient Reinforcement Learning—0
Parametrized quantum policies for reinforcement learning—0
Solipsistic Reinforcement Learning—0
Resolving Causal Confusion in Reinforcement Learning via Robust Exploration—0
Minimum Description Length Skills for Accelerated Reinforcement Learning—0
Out-of-distribution generalization of internal models is correlated with reward—0
Vision-Based Mobile Robotics Obstacle Avoidance With Deep Reinforcement Learning—0
Provably Efficient Cooperative Multi-Agent Reinforcement Learning with Function Approximation—0
Real-world Ride-hailing Vehicle Repositioning using Deep Reinforcement Learning—0
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Benchmark Results

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