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 2611–2620 of 15113 papers

TitleStatusHype
Human-AI Collaboration in Real-World Complex Environment with Reinforcement Learning—0
Reinforcement Learning for Safe Occupancy Strategies in Educational Spaces during an Epidemic—0
Multiagent Copilot Approach for Shared Autonomy between Human EEG and TD3 Deep Reinforcement Learning—0
A Survey of Reinforcement Learning from Human Feedback—0
REBEL: Reward Regularization-Based Approach for Robotic Reinforcement Learning from Human Feedback—0
Pangu-Agent: A Fine-Tunable Generalist Agent with Structured Reasoning—0
Maximum entropy GFlowNets with soft Q-learning—0
Diffusion Reward: Learning Rewards via Conditional Video DiffusionCode1
Multi-Agent Probabilistic Ensembles with Trajectory Sampling for Connected Autonomous Vehicles—0
Critic-Guided Decision Transformer for Offline Reinforcement LearningCode1
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Benchmark Results

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