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 5826–5850 of 15113 papers

TitleStatusHype
Learning to Communicate with Intent: An Introduction—0
A Reinforcement Learning Approach for Process Parameter Optimization in Additive Manufacturing—0
DexPoint: Generalizable Point Cloud Reinforcement Learning for Sim-to-Real Dexterous Manipulation—0
AlphaSnake: Policy Iteration on a Nondeterministic NP-hard Markov Decision Process—0
Planning Irregular Object Packing via Hierarchical Reinforcement Learning—0
Solar Power driven EV Charging Optimization with Deep Reinforcement Learning—0
Reward Gaming in Conditional Text Generation—0
Model Based Residual Policy Learning with Applications to Antenna Control—0
Minimum information divergence of Q-functions for dynamic treatment resumes—0
Addressing the issue of stochastic environments and local decision-making in multi-objective reinforcement learning—0
Data-pooling Reinforcement Learning for Personalized Healthcare Intervention—0
General Intelligence Requires Rethinking Exploration—0
Explainable Action Advising for Multi-Agent Reinforcement LearningCode0
Agent-State Construction with Auxiliary InputsCode0
Contextual Transformer for Offline Meta Reinforcement Learning—0
APT: Adaptive Perceptual quality based camera Tuning using reinforcement learning—0
Universal Distributional Decision-based Black-box Adversarial Attack with Reinforcement Learning—0
Reinforcement Learning Methods for Wordle: A POMDP/Adaptive Control Approach—0
Offline Reinforcement Learning with Adaptive Behavior Regularization—0
Reinforcement Learning Based Resource Allocation for Network Slices in O-RAN Midhaul—0
(When) Are Contrastive Explanations of Reinforcement Learning Helpful?—0
Parallel Automatic History Matching Algorithm Using Reinforcement Learning—0
NeurIPS 2022 Competition: Driving SMARTS—0
Linear Reinforcement Learning with Ball Structure Action Space—0
Hierarchically Structured Task-Agnostic Continual LearningCode0
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Benchmark Results

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