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 1361–1370 of 15113 papers

TitleStatusHype
Interpretable Concept Bottlenecks to Align Reinforcement Learning AgentsCode1
Interpretable End-to-end Urban Autonomous Driving with Latent Deep Reinforcement LearningCode1
A Reinforcement Learning Based Encoder-Decoder Framework for Learning Stock Trading RulesCode1
CORA: Benchmarks, Baselines, and Metrics as a Platform for Continual Reinforcement Learning AgentsCode1
Adversarial Soft Advantage Fitting: Imitation Learning without Policy OptimizationCode1
Co-Reinforcement Learning for Unified Multimodal Understanding and GenerationCode1
Inverse Constrained Reinforcement LearningCode1
Correlation-aware Cooperative Multigroup Broadcast 360° Video Delivery Network: A Hierarchical Deep Reinforcement Learning ApproachCode1
A Deep Reinforcement Learning Approach for Solving the Traveling Salesman Problem with DroneCode1
BabyAI 1.1Code1
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Benchmark Results

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