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 7876–7900 of 15113 papers

TitleStatusHype
AWD3: Dynamic Reduction of the Estimation Bias—0
Cooperative multi-agent reinforcement learning for high-dimensional nonequilibrium controlCode0
DriverGym: Democratising Reinforcement Learning for Autonomous Driving—0
Causal Multi-Agent Reinforcement Learning: Review and Open Problems—0
Improving Experience Replay through Modeling of Similar Transitions' SetsCode0
CubeTR: Learning to Solve The Rubiks Cube Using Transformers—0
Adapting Surprise Minimizing Reinforcement Learning Techniques for Transactive Control—0
Agent Spaces—0
Towards Robust Knowledge Graph Embedding via Multi-task Reinforcement Learning—0
Model-Based Reinforcement Learning via Stochastic Hybrid Models—0
Multi-agent Reinforcement Learning for Cooperative Lane Changing of Connected and Autonomous Vehicles in Mixed Traffic—0
Spatially and Seamlessly Hierarchical Reinforcement Learning for State Space and Policy space in Autonomous Driving—0
DeCOM: Decomposed Policy for Constrained Cooperative Multi-Agent Reinforcement LearningCode0
Look Before You Leap: Safe Model-Based Reinforcement Learning with Human Intervention—0
Dealing with the Unknown: Pessimistic Offline Reinforcement Learning—0
HARPO: Learning to Subvert Online Behavioral Advertising—0
Risk Sensitive Model-Based Reinforcement Learning using Uncertainty Guided Planning—0
Safe Policy Optimization with Local Generalized Linear Function ApproximationsCode0
On Assessing The Safety of Reinforcement Learning algorithms Using Formal Methods—0
Interactive Inverse Reinforcement Learning for Cooperative Games—0
Batch Reinforcement Learning from Crowds—0
Dueling RL: Reinforcement Learning with Trajectory Preferences—0
Explainable Deep Reinforcement Learning for Portfolio Management: An Empirical Approach—0
FinRL-Podracer: High Performance and Scalable Deep Reinforcement Learning for Quantitative Finance—0
Automatic Goal Generation using Dynamical Distance Learning—0
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Benchmark Results

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