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 5851–5875 of 15113 papers

TitleStatusHype
Dynamic Collaborative Multi-Agent Reinforcement Learning Communication for Autonomous Drone Reforestation—0
Towards Abstractive Timeline Summarisation using Preference-based Reinforcement LearningCode0
Identifying Coordination in a Cognitive Radar Network -- A Multi-Objective Inverse Reinforcement Learning Approach—0
Goal-Conditioned Reinforcement Learning in the Presence of an Adversary—0
Multi-Agent Deep Reinforcement Learning for Efficient Passenger Delivery in Urban Air Mobility—0
Rewards Encoding Environment Dynamics Improves Preference-based Reinforcement Learning—0
Deep Reinforcement Learning with Vector Quantized Encoding—0
CACTO: Continuous Actor-Critic with Trajectory Optimization -- Towards global optimality—0
Controlling Commercial Cooling Systems Using Reinforcement Learning—0
Efficient Deep Reinforcement Learning with Predictive Processing Proximal Policy OptimizationCode0
Emergency action termination for immediate reaction in hierarchical reinforcement learning—0
Deep Reinforcement Learning Microgrid Optimization Strategy Considering Priority Flexible Demand Side—0
Global and Local Analysis of Interestingness for Competency-Aware Deep Reinforcement LearningCode0
Gradient Imitation Reinforcement Learning for General Low-Resource Information Extraction—0
Efficient Domain Coverage for Vehicles with Second-Order Dynamics via Multi-Agent Reinforcement Learning—0
Job Scheduling in Datacenters using Constraint Controlled RL—0
When is Realizability Sufficient for Off-Policy Reinforcement Learning?—0
Robust N-1 secure HV Grid Flexibility Estimation for TSO-DSO coordinated Congestion Management with Deep Reinforcement Learning—0
Solving Collaborative Dec-POMDPs with Deep Reinforcement Learning Heuristics—0
Deep W-Networks: Solving Multi-Objective Optimisation Problems With Deep Reinforcement LearningCode0
Interpretable Deep Reinforcement Learning for Green Security Games with Real-Time Information—0
Leveraging Offline Data in Online Reinforcement Learning—0
Foundation Models for Semantic Novelty in Reinforcement Learning—0
Leveraging Sequentiality in Reinforcement Learning from a Single DemonstrationCode0
Efficient Compressed Ratio Estimation Using Online Sequential Learning for Edge Computing—0
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Benchmark Results

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