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 10351–10375 of 15113 papers

TitleStatusHype
Accelerated Deep Reinforcement Learning Based Load Shedding for Emergency Voltage Control—0
Efficient Sampling-Based Maximum Entropy Inverse Reinforcement Learning with Application to Autonomous Driving—0
dm_control: Software and Tasks for Continuous Control—0
Graph Neural Networks and Reinforcement Learning for Behavior Generation in Semantic EnvironmentsCode1
Risk-Sensitive Reinforcement Learning: Near-Optimal Risk-Sample Tradeoff in Regret—0
Sample-Efficient Reinforcement Learning of Undercomplete POMDPs—0
QTRAN++: Improved Value Transformation for Cooperative Multi-Agent Reinforcement Learning—0
Safe Reinforcement Learning via Curriculum InductionCode1
Provably Efficient Causal Reinforcement Learning with Confounded Observational Data—0
Near-Optimal Reinforcement Learning with Self-Play—0
Learning with AMIGo: Adversarially Motivated Intrinsic GoalsCode1
Ecological Reinforcement Learning—0
Constrained Combinatorial Optimization with Reinforcement Learning—0
Hierarchical Reinforcement Learning for Deep Goal Reasoning: An Expressiveness Analysis—0
Reinforcement Learning for Mean Field Games with Strategic Complementarities—0
Gradient-EM Bayesian Meta-learning—0
Automated Optical Multi-layer Design via Deep Reinforcement LearningCode0
Breaking the Curse of Many Agents: Provable Mean Embedding Q-Iteration for Mean-Field Reinforcement Learning—0
Sample Factory: Egocentric 3D Control from Pixels at 100000 FPS with Asynchronous Reinforcement LearningCode1
Off-Policy Self-Critical Training for Transformer in Visual Paragraph Generation—0
Towards Tractable Optimism in Model-Based Reinforcement Learning—0
Robust Reinforcement Learning using Least Squares Policy Iteration with Provable Performance Guarantees—0
Entropic Risk Constrained Soft-Robust Policy Optimization—0
Accelerating Safe Reinforcement Learning with Constraint-mismatched Policies—0
Langevin Dynamics for Adaptive Inverse Reinforcement Learning of Stochastic Gradient Algorithms—0
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Benchmark Results

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