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 11326–11350 of 15113 papers

TitleStatusHype
Hierarchical Reinforcement Learning: Approximating Optimal Discounted TSP Using Local Policies—0
Hierarchical Reinforcement Learning and Value Optimization for Challenging Quadruped Locomotion—0
Hierarchical Reinforcement Learning as a Model of Human Task Interleaving—0
Hierarchical Reinforcement Learning Based Video Semantic Coding for Segmentation—0
Hierarchical Reinforcement Learning for Quadruped Locomotion—0
Hierarchical Reinforcement Learning for Optimal Agent Grouping in Cooperative Systems—0
Decomposability and Parallel Computation of Multi-Agent LQR—0
Hierarchical reinforcement learning for efficient exploration and transfer—0
Hierarchical reinforcement learning for efficent exploration and transfer—0
Hierarchical Reinforcement Learning for Precise Soccer Shooting Skills using a Quadrupedal Robot—0
Hierarchical Reinforcement Learning for Furniture Layout in Virtual Indoor Scenes—0
Hierarchical Reinforcement Learning for Air-to-Air Combat—0
Hierarchical Reinforcement Learning for Deep Goal Reasoning: An Expressiveness Analysis—0
Hierarchical Reinforcement Learning for Multi-agent MOBA Game—0
Hierarchical Reinforcement Learning for Open-Domain Dialog—0
Hierarchical Reinforcement Learning for RIS-Assisted Energy-Efficient RAN—0
Hierarchical Reinforcement Learning Framework for Stochastic Spaceflight Campaign Design—0
Hierarchical Reinforcement Learning in Complex 3D Environments—0
Hierarchical Reinforcement Learning Method for Autonomous Vehicle Behavior Planning—0
Hierarchical Reinforcement Learning of Locomotion Policies in Response to Approaching Objects: A Preliminary Study—0
Hierarchical Reinforcement Learning with Abductive Planning—0
Hierarchical Reinforcement Learning with Hindsight—0
Hierarchical Reinforcement Learning with Opponent Modeling for Distributed Multi-agent Cooperation—0
Hierarchical Reinforcement Learning with Deep Nested Agents—0
Hierarchical RL-MPC for Demand Response Scheduling—0
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Benchmark Results

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