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 3276–3300 of 15113 papers

TitleStatusHype
DDPG based on multi-scale strokes for financial time series trading strategy—0
Modified DDPG car-following model with a real-world human driving experience with CARLA simulator—0
DDPG++: Striving for Simplicity in Continuous-control Off-Policy Reinforcement Learning—0
A Fast Convergence Theory for Offline Decision Making—0
Dealing with Limited Backhaul Capacity in Millimeter Wave Systems: A Deep Reinforcement Learning Approach—0
Dealing with Non-Stationarity in Multi-Agent Deep Reinforcement Learning—0
A Hybrid Approach for Reinforcement Learning Using Virtual Policy Gradient for Balancing an Inverted Pendulum—0
Dealing with Sparse Rewards Using Graph Neural Networks—0
Dealing with the Unknown: Pessimistic Offline Reinforcement Learning—0
DEALIO: Data-Efficient Adversarial Learning for Imitation from Observation—0
INTAGS: Interactive Agent-Guided Simulation—0
Agent Modeling as Auxiliary Task for Deep Reinforcement Learning—0
Death and Suicide in Universal Artificial Intelligence—0
A SUMO Framework for Deep Reinforcement Learning Experiments Solving Electric Vehicle Charging Dispatching Problem—0
De-Biased Modelling of Search Click Behavior with Reinforcement Learning—0
DEAR: Deep Reinforcement Learning for Online Advertising Impression in Recommender Systems—0
Deep Reinforcement Learning for Online Control of Stochastic Partial Differential Equations—0
Decentralized Automotive Radar Spectrum Allocation to Avoid Mutual Interference Using Reinforcement Learning—0
Decentralized Circle Formation Control for Fish-like Robots in the Real-world via Reinforcement Learning—0
A Succinct Summary of Reinforcement Learning—0
Deep Reinforcement Learning for NLP—0
A Gentle Lecture Note on Filtrations in Reinforcement Learning—0
On Improving Model-Free Algorithms for Decentralized Multi-Agent Reinforcement Learning—0
Decentralized Cooperative Reinforcement Learning with Hierarchical Information Structure—0
A Subgame Perfect Equilibrium Reinforcement Learning Approach to Time-inconsistent Problems—0
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Benchmark Results

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