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 1851–1860 of 15113 papers

TitleStatusHype
Decentralized Semantic Traffic Control in AVs Using RL and DQN for Dynamic Roadblocks—0
Combining Automated Optimisation of Hyperparameters and Reward ShapeCode0
Preference Elicitation for Offline Reinforcement Learning—0
Reinforcement Learning with Intrinsically Motivated Feedback Graph for Lost-sales Inventory Control—0
GenRL: Multimodal-foundation world models for generalization in embodied agentsCode2
ARES: Alternating Reinforcement Learning and Supervised Fine-Tuning for Enhanced Multi-Modal Chain-of-Thought Reasoning Through Diverse AI FeedbackCode0
EXTRACT: Efficient Policy Learning by Extracting Transferable Robot Skills from Offline Data—0
Human-Object Interaction from Human-Level Instructions—0
Privacy Preserving Reinforcement Learning for Population Processes—0
Leveraging Reinforcement Learning in Red Teaming for Advanced Ransomware Attack Simulations—0
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Benchmark Results

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