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 2371–2380 of 15113 papers

TitleStatusHype
Batch Reinforcement Learning Through Continuation Method—0
Batch Reinforcement Learning on the Industrial Benchmark: First Experiences—0
An Adaptable Approach to Learn Realistic Legged Locomotion without Examples—0
Adaptive Stochastic Nonlinear Model Predictive Control with Look-ahead Deep Reinforcement Learning for Autonomous Vehicle Motion Control—0
Batch Reinforcement Learning from Crowds—0
Batch Recurrent Q-Learning for Backchannel Generation Towards Engaging Agents—0
An A* Curriculum Approach to Reinforcement Learning for RGBD Indoor Robot Navigation—0
Batch Policy Gradient Methods for Improving Neural Conversation Models—0
Learning "What-if" Explanations for Sequential Decision-Making—0
An Actor-Critic Method for Simulation-Based Optimization—0
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Benchmark Results

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