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 10226–10250 of 15113 papers

TitleStatusHype
Discourse Coherence, Reference Grounding and Goal Oriented Dialogue—0
A Natural Actor-Critic Algorithm with Downside Risk Constraints—0
Auto-MAP: A DQN Framework for Exploring Distributed Execution Plans for DNN Workloads—0
Responsive Safety in Reinforcement Learning by PID Lagrangian Methods—0
A deep reinforcement learning model based on deterministic policy gradient for collective neural crest cell migration—0
Deep Reinforcement Learning with Interactive Feedback in a Human-Robot Environment—0
Deep Reinforcement Learning and its Neuroscientific Implications—0
Cognitive Radio Network Throughput Maximization with Deep Reinforcement Learning—0
Necessary and Sufficient Conditions for Inverse Reinforcement Learning of Bayesian Stopping Time Problems—0
Towards a practical measure of interference for reinforcement learning—0
Predictive Maintenance for Edge-Based Sensor Networks: A Deep Reinforcement Learning Approach—0
Near-Optimal Provable Uniform Convergence in Offline Policy Evaluation for Reinforcement Learning—0
Provably Safe PAC-MDP Exploration Using AnalogiesCode1
Sharp Analysis of Smoothed Bellman Error Embedding—0
The LoCA Regret: A Consistent Metric to Evaluate Model-Based Behavior in Reinforcement LearningCode0
Efficient Connected and Automated Driving System with Multi-agent Graph Reinforcement Learning—0
Consensus Multi-Agent Reinforcement Learning for Volt-VAR Control in Power Distribution Networks—0
Enhancing SAT solvers with glue variable predictionsCode1
LFQ: Online Learning of Per-flow Queuing Policies using Deep Reinforcement LearningCode1
Counterfactual Data Augmentation using Locally Factored DynamicsCode1
Learning Implicit Credit Assignment for Cooperative Multi-Agent Reinforcement LearningCode1
Integrating Distributed Architectures in Highly Modular RL LibrariesCode0
Maximum Entropy Gain Exploration for Long Horizon Multi-goal Reinforcement LearningCode1
Meta-Learning through Hebbian Plasticity in Random NetworksCode1
Mission schedule of agile satellites based on Proximal Policy Optimization Algorithm—0
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Benchmark Results

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