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 5051–5075 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
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Benchmark Results

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