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 5051–5100 of 15113 papers

TitleStatusHype
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
Hierarchical RNNs-Based Transformers MADDPG for Mixed Cooperative-Competitive Environments—0
Hierarchical Strategies for Cooperative Multi-Agent Reinforcement Learning—0
Ensemble Reinforcement Learning in Continuous Spaces -- A Hierarchical Multi-Step Approach for Policy Training—0
Hierarchies of Planning and Reinforcement Learning for Robot Navigation—0
Hierarchy through Composition with Linearly Solvable Markov Decision Processes—0
Hierarchy Through Composition with Multitask LMDPs—0
High Acceleration Reinforcement Learning for Real-World Juggling with Binary Rewards—0
High-Accuracy Model-Based Reinforcement Learning, a Survey—0
High-confidence error estimates for learned value functions—0
Optimizing Percentile Criterion Using Robust MDPs—0
High-dimensional Bid Learning for Energy Storage Bidding in Energy Markets—0
MERL: Multi-Head Reinforcement Learning—0
High-Dimensional Stock Portfolio Trading with Deep Reinforcement Learning—0
Reinforcement learning with world model—0
Higher Replay Ratio Empowers Sample-Efficient Multi-Agent Reinforcement Learning—0
High-level Decisions from a Safe Maneuver Catalog with Reinforcement Learning for Safe and Cooperative Automated Merging—0
High-Level Strategy Selection under Partial Observability in StarCraft: Brood War—0
High Performance Simulation for Scalable Multi-Agent Reinforcement Learning—0
High-Precision Geosteering via Reinforcement Learning and Particle Filters—0
High Quality Related Search Query Suggestions using Deep Reinforcement Learning—0
High-speed Autonomous Drifting with Deep Reinforcement Learning—0
HighwayLLM: Decision-Making and Navigation in Highway Driving with RL-Informed Language Model—0
Highway Reinforcement Learning—0
HiLight: A Hierarchical Reinforcement Learning Framework with Global Adversarial Guidance for Large-Scale Traffic Signal Control—0
Hill Climbing on Value Estimates for Search-control in Dyna—0
Hindsight Curriculum Generation Based Multi-Goal Experience Replay—0
Hindsight Expectation Maximization for Goal-conditioned Reinforcement Learning—0
Hindsight Generative Adversarial Imitation Learning—0
Hindsight Reward Tweaking via Conditional Deep Reinforcement Learning—0
Hindsight States: Blending Sim and Real Task Elements for Efficient Reinforcement Learning—0
Neural PPO-Clip Attains Global Optimality: A Hinge Loss Perspective—0
HIPPOCAMPAL NEURONAL REPRESENTATIONS IN CONTINUAL LEARNING—0
Historical Text Normalization with Delayed Rewards—0
Hit and Lead Discovery with Explorative RL and Fragment-based Molecule Generation—0
Lagrangian-based online safe reinforcement learning for state-constrained systems—0
HJB Optimal Feedback Control with Deep Differential Value Functions and Action Constraints—0
HLIC: Harmonizing Optimization Metrics in Learned Image Compression by Reinforcement Learning—0
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Benchmark Results

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