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

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Showing 11351–11375 of 15113 papers

TitleStatusHype
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
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Benchmark Results

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