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 501–510 of 15113 papers

TitleStatusHype
StablePrompt: Automatic Prompt Tuning using Reinforcement Learning for Large Language ModelsCode1
Retrieval-Augmented Decision Transformer: External Memory for In-context RLCode1
Coevolving with the Other You: Fine-Tuning LLM with Sequential Cooperative Multi-Agent Reinforcement LearningCode1
GreenLight-Gym: Reinforcement learning benchmark environment for control of greenhouse production systemsCode1
Mitigating Adversarial Perturbations for Deep Reinforcement Learning via Vector QuantizationCode1
Predictive Coding for Decision TransformerCode1
ReLIC: A Recipe for 64k Steps of In-Context Reinforcement Learning for Embodied AICode1
Scaling Offline Model-Based RL via Jointly-Optimized World-Action Model PretrainingCode1
CurricuLLM: Automatic Task Curricula Design for Learning Complex Robot Skills using Large Language ModelsCode1
ARLBench: Flexible and Efficient Benchmarking for Hyperparameter Optimization in Reinforcement LearningCode1
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Benchmark Results

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