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 7001–7025 of 15113 papers

TitleStatusHype
Towards Evaluating Adaptivity of Model-Based Reinforcement Learning MethodsCode0
Multi-objective Pointer Network for Combinatorial OptimizationCode0
Deep Reinforcement Learning for Online Routing of Unmanned Aerial Vehicles with Wireless Power Transfer—0
Deep Reinforcement Learning for Orienteering Problems Based on Decomposition—0
Deep Reinforcement Learning-based Radio Resource Allocation and Beam Management under Location Uncertainty in 5G mmWave Networks—0
Graph Neural Network based Agent in Google Research Football—0
Finite-Time Analysis of Temporal Difference Learning: Discrete-Time Linear System Perspective—0
TASAC: a twin-actor reinforcement learning framework with stochastic policy for batch process control—0
Revisiting Gaussian mixture critics in off-policy reinforcement learning: a sample-based approach—0
Resilient robot teams: a review integrating decentralised control, change-detection, and learning—0
Optimizing Nitrogen Management with Deep Reinforcement Learning and Crop Simulations—0
Learning how to Interact with a Complex Interface using Hierarchical Reinforcement Learning—0
A Hierarchical Bayesian Approach to Inverse Reinforcement Learning with Symbolic Reward Machines—0
Federated Learning for Distributed Energy-Efficient Resource Allocation—0
Joint Learning of Reward Machines and Policies in Environments with Partially Known Semantics—0
Reinforcement Learning from Partial Observation: Linear Function Approximation with Provable Sample Efficiency—0
Reinforcement Learning with Intrinsic Affinity for Personalized Prosperity Management—0
Mingling Foresight with Imagination: Model-Based Cooperative Multi-Agent Reinforcement Learning—0
SAAC: Safe Reinforcement Learning as an Adversarial Game of Actor-Critics—0
Understanding and Preventing Capacity Loss in Reinforcement Learning—0
Network Topology Optimization via Deep Reinforcement Learning—0
When Is Partially Observable Reinforcement Learning Not Scary?—0
Training and Evaluation of Deep Policies using Reinforcement Learning and Generative Models—0
Optimizing Tensor Network Contraction Using Reinforcement Learning—0
INFOrmation Prioritization through EmPOWERment in Visual Model-Based RL—0
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Benchmark Results

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