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 10676–10700 of 15113 papers

TitleStatusHype
Deep Reinforcement Learning with Interactive Feedback in a Human-Robot Environment—0
Cognitive Radio Network Throughput Maximization with Deep Reinforcement Learning—0
Deep Reinforcement Learning and its Neuroscientific Implications—0
Consensus Multi-Agent Reinforcement Learning for Volt-VAR Control in Power Distribution Networks—0
Integrating Distributed Architectures in Highly Modular RL LibrariesCode0
Efficient Connected and Automated Driving System with Multi-agent Graph Reinforcement Learning—0
Unsupervised Paraphrasing via Deep Reinforcement Learning—0
Mission schedule of agile satellites based on Proximal Policy Optimization Algorithm—0
Decentralized Reinforcement Learning: Global Decision-Making via Local Economic Transactions—0
Discount Factor as a Regularizer in Reinforcement LearningCode0
Variational Policy Gradient Method for Reinforcement Learning with General Utilities—0
Strategies for Using Proximal Policy Optimization in Mobile Puzzle Games—0
Temporal-Logic-Based Reward Shaping for Continuing Reinforcement Learning Tasks—0
Hedging using reinforcement learning: Contextual k-Armed Bandit versus Q-learning—0
A Unifying View of Optimism in Episodic Reinforcement Learning—0
An Autonomous Free Airspace En-route Controller using Deep Reinforcement Learning Techniques—0
A Conceptual Framework for Externally-influenced Agents: An Assisted Reinforcement Learning Review—0
Learning to search efficiently for causally near-optimal treatmentsCode0
ε-BMC: A Bayesian Ensemble Approach to Epsilon-Greedy Exploration in Model-Free Reinforcement LearningCode0
Decentralized Deep Reinforcement Learning for Network Level Traffic Signal Control—0
Deep reinforcement learning driven inspection and maintenance planning under incomplete information and constraints—0
Human-centered collaborative robots with deep reinforcement learning—0
Learning "What-if" Explanations for Sequential Decision-Making—0
BOSH: Bayesian Optimization by Sampling Hierarchically—0
Robust Inverse Reinforcement Learning under Transition Dynamics MismatchCode0
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Benchmark Results

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