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 3091–3100 of 15113 papers

TitleStatusHype
From Explainability to Interpretability: Interpretable Policies in Reinforcement Learning Via Model Explanation—0
Average-Reward Reinforcement Learning with Entropy Regularization—0
Reinforcement Learning-Enhanced Procedural Generation for Dynamic Narrative-Driven AR Experiences—0
Projection Implicit Q-Learning with Support Constraint for Offline Reinforcement Learning—0
Hybrid Action Based Reinforcement Learning for Multi-Objective Compatible Autonomous Driving—0
Decision Transformers for RIS-Assisted Systems with Diffusion Model-Based Channel Acquisition—0
CHEQ-ing the Box: Safe Variable Impedance Learning for Robotic PolishingCode0
FDPP: Fine-tune Diffusion Policy with Human Preference—0
Enhancing Online Reinforcement Learning with Meta-Learned Objective from Offline DataCode0
Combining LLM decision and RL action selection to improve RL policy for adaptive interventions—0
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Benchmark Results

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