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 8201–8225 of 15113 papers

TitleStatusHype
Pedestrian Prediction by Planning using Deep Neural Networks—0
Penalized Proximal Policy Optimization for Safe Reinforcement Learning—0
PEORL: Integrating Symbolic Planning and Hierarchical Reinforcement Learning for Robust Decision-Making—0
Perception and Navigation in Autonomous Systems in the Era of Learning: A Survey—0
Perception-Prediction-Reaction Agents for Deep Reinforcement Learning—0
Perceptual Reward Functions—0
Perceptual Values from Observation—0
Performance Bounds for Policy-Based Average Reward Reinforcement Learning Algorithms—0
Performance-Driven Controller Tuning via Derivative-Free Reinforcement Learning—0
Performance Dynamics and Termination Errors in Reinforcement Learning: A Unifying Perspective—0
Performance Optimization for Semantic Communications: An Attention-based Reinforcement Learning Approach—0
Performance Optimization for Variable Bitwidth Federated Learning in Wireless Networks—0
Performance-Weighed Policy Sampling for Meta-Reinforcement Learning—0
Performative Reinforcement Learning—0
PERIL: Probabilistic Embeddings for hybrid Meta-Reinforcement and Imitation Learning—0
Periodic agent-state based Q-learning for POMDPs—0
PeRL: Permutation-Enhanced Reinforcement Learning for Interleaved Vision-Language Reasoning—0
PerSim: Data-Efficient Offline Reinforcement Learning with Heterogeneous Agents via Personalized Simulators—0
Autonomous Reinforcement Learning via Subgoal Curricula—0
Persistent Rule-based Interactive Reinforcement Learning—0
Towards Personalization of User Preferences in Partially Observable Smart Home Environments—0
Personalisation via Dynamic Policy Fusion—0
Personalization for Web-based Services using Offline Reinforcement Learning—0
A clustering-based reinforcement learning approach for tailored personalization of e-Health interventions—0
Personalization of Hearing Aid Compression by Human-In-Loop Deep Reinforcement Learning—0
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Benchmark Results

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