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 10026–10050 of 15113 papers

TitleStatusHype
Distributed Reinforcement Learning for Robot Teams: A Review—0
Distributed Reinforcement Learning of Targeted Grasping with Active Vision for Mobile Manipulators—0
Distributed Reinforcement Learning via Gossip—0
Distributed Structured Actor-Critic Reinforcement Learning for Universal Dialogue Management—0
Distributed traffic light control at uncoupled intersections with real-world topology by deep reinforcement learning—0
Distributed Uplink Beamforming in Cell-Free Networks Using Deep Reinforcement Learning—0
Distributed Value Function Approximation for Collaborative Multi-Agent Reinforcement Learning—0
Distributed Wildfire Surveillance with Autonomous Aircraft using Deep Reinforcement Learning—0
Distributional Actor-Critic Ensemble for Uncertainty-Aware Continuous Control—0
Distributional Decision Transformer for Hindsight Information Matching—0
Distributional Hamilton-Jacobi-Bellman Equations for Continuous-Time Reinforcement Learning—0
Distributionally Adaptive Meta Reinforcement Learning—0
Distributionally-Constrained Policy Optimization via Unbalanced Optimal Transport—0
Distributionally Robust Imitation Learning—0
Distributionally Robust Model-Based Offline Reinforcement Learning with Near-Optimal Sample Complexity—0
Distributionally Robust Offline Reinforcement Learning with Linear Function Approximation—0
Distributionally Robust Reinforcement Learning—0
Distributionally Robust Reinforcement Learning with Interactive Data Collection: Fundamental Hardness and Near-Optimal Algorithm—0
Distributional Method for Risk Averse Reinforcement Learning—0
Distributional Perturbation for Efficient Exploration in Distributional Reinforcement Learning—0
Distributional Reinforcement Learning for Efficient Exploration—0
Distributional Reinforcement Learning for Risk-Sensitive Policies—0
Distributional Reinforcement Learning for mmWave Communications with Intelligent Reflectors on a UAV—0
Distributional Reinforcement Learning for Scheduling of Chemical Production Processes—0
Distributional reinforcement learning with linear function approximation—0
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Benchmark Results

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