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 4701–4725 of 15113 papers

TitleStatusHype
Neural Distillation as a State Representation Bottleneck in Reinforcement Learning—0
On Neural Consolidation for Transfer in Reinforcement Learning—0
Visual Backtracking Teleoperation: A Data Collection Protocol for Offline Image-Based Reinforcement Learning—0
Maximum-Likelihood Inverse Reinforcement Learning with Finite-Time Guarantees—0
Stateful active facilitator: Coordination and Environmental Heterogeneity in Cooperative Multi-Agent Reinforcement LearningCode0
DISCOVER: Deep identification of symbolically concise open-form PDEs via enhanced reinforcement-learningCode1
Learning Dynamic Abstract Representations for Sample-Efficient Reinforcement Learning—0
Learning Perception-Aware Agile Flight in Cluttered Environments—0
Using Deep Reinforcement Learning for mmWave Real-Time Scheduling—0
Evaluating Disentanglement in Generative Models Without Knowledge of Latent Factors—0
Handling Sparse Rewards in Reinforcement Learning Using Model Predictive Control—0
Hyperbolic Deep Reinforcement Learning—0
Federated Reinforcement Learning for Real-Time Electric Vehicle Charging and Discharging Control—0
CostNet: An End-to-End Framework for Goal-Directed Reinforcement Learning—0
Interpretable Option Discovery using Deep Q-Learning and Variational Autoencoders—0
Is Reinforcement Learning (Not) for Natural Language Processing: Benchmarks, Baselines, and Building Blocks for Natural Language Policy OptimizationCode1
CaiRL: A High-Performance Reinforcement Learning Environment ToolkitCode1
Square-root regret bounds for continuous-time episodic Markov decision processes—0
Policy Gradient for Reinforcement Learning with General Utilities—0
MSRL: Distributed Reinforcement Learning with Dataflow Fragments—0
Offline Reinforcement Learning with Differentiable Function Approximation is Provably Efficient—0
Near-Optimal Deployment Efficiency in Reward-Free Reinforcement Learning with Linear Function Approximation—0
Mastering Spatial Graph Prediction of Road Networks—0
Latent State Marginalization as a Low-cost Approach for Improving ExplorationCode1
Accelerate Reinforcement Learning with PID Controllers in the Pendulum SimulationsCode0
Show:102550
← PrevPage 189 of 605Next →

Benchmark Results

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