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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 6876–6900 of 15113 papers

TitleStatusHype
Long Run Incremental Cost (LRIC) Distribution Network Pricing in UK, advising China's Distribution Network—0
Prototyping three key properties of specific curiosity in computational reinforcement learning—0
On Jointly Optimizing Partial Offloading and SFC Mapping: A Cooperative Dual-agent Deep Reinforcement Learning Approach—0
Towards biologically plausible Dreaming and Planning in recurrent spiking networksCode0
Survey on Fair Reinforcement Learning: Theory and Practice—0
Learning Progress Driven Multi-Agent CurriculumCode0
Synthesis from Satisficing and Temporal GoalsCode0
Sparse Adversarial Attack in Multi-agent Reinforcement Learning—0
Reinforcement Learning with Brain-Inspired Modulation can Improve Adaptation to Environmental ChangesCode0
Towards Applicable Reinforcement Learning: Improving the Generalization and Sample Efficiency with Policy Ensemble—0
Routing and Placement of Macros using Deep Reinforcement Learning—0
Parallel bandit architecture based on laser chaos for reinforcement learning—0
Data Valuation for Offline Reinforcement Learning—0
IL-flOw: Imitation Learning from Observation using Normalizing Flows—0
Deconfounding Actor-Critic Network with Policy Adaptation for Dynamic Treatment RegimesCode0
AIGenC: An AI generalisation model via creativity—0
Distributed Multi-Agent Deep Reinforcement Learning for Robust Coordination against Noise—0
Deep Reinforcement Learning Based on Location-Aware Imitation Environment for RIS-Aided mmWave MIMO Systems—0
Generating Explanations from Deep Reinforcement Learning Using Episodic Memory—0
No More Pesky Hyperparameters: Offline Hyperparameter Tuning for RL—0
Policy Distillation with Selective Input Gradient Regularization for Efficient Interpretability—0
Optimal Adaptive Prediction Intervals for Electricity Load Forecasting in Distribution Systems via Reinforcement LearningCode0
Market Making via Reinforcement Learning in China Commodity Market—0
World Value Functions: Knowledge Representation for Multitask Reinforcement Learning—0
Slowly Changing Adversarial Bandit Algorithms are Efficient for Discounted MDPs—0
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Benchmark Results

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