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 2721–2730 of 15113 papers

TitleStatusHype
Automatic Curriculum Learning for Driving Scenarios: Towards Robust and Efficient Reinforcement Learning—0
Aligning Humans and Robots via Reinforcement Learning from Implicit Human Feedback—0
Adaptive Genomic Evolution of Neural Network Topologies (AGENT) for State-to-Action Mapping in Autonomous Agents—0
Automatic Curriculum Learning For Deep RL: A Short Survey—0
Automatic Curricula via Expert Demonstrations—0
Automatic Bridge Bidding Using Deep Reinforcement Learning—0
Automatically Learning Fallback Strategies with Model-Free Reinforcement Learning in Safety-Critical Driving Scenarios—0
A Bayesian Approach to Learning Bandit Structure in Markov Decision Processes—0
PARL: A Unified Framework for Policy Alignment in Reinforcement Learning from Human Feedback—0
Automated vehicle's behavior decision making using deep reinforcement learning and high-fidelity simulation environment—0
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Benchmark Results

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