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 10726–10750 of 15113 papers

TitleStatusHype
Exponential Bellman Equation and Improved Regret Bounds for Risk-Sensitive Reinforcement Learning—0
Exponential Hardness of Reinforcement Learning with Linear Function Approximation—0
Exponential improvements for quantum-accessible reinforcement learning—0
Exponential Lower Bounds for Batch Reinforcement Learning: Batch RL can be Exponentially Harder than Online RL—0
Exponentially Weighted Imitation Learning for Batched Historical Data—0
Exposing Surveillance Detection Routes via Reinforcement Learning, Attack Graphs, and Cyber Terrain—0
Exposure-Based Multi-Agent Inspection of a Tumbling Target Using Deep Reinforcement Learning—0
A Tractable Inference Perspective of Offline RL—0
Expressiveness in Deep Reinforcement Learning—0
Extendable NFV-Integrated Control Method Using Reinforcement Learning—0
Extend Adversarial Policy Against Neural Machine Translation via Unknown Token—0
Extended Radial Basis Function Controller for Reinforcement Learning—0
Extending a Quantum Reinforcement Learning Exploration Policy with Flags to Connect Four—0
Extending Deep Reinforcement Learning Frameworks in Cryptocurrency Market Making—0
External control of a genetic toggle switch via Reinforcement Learning—0
EXTRACT: Efficient Policy Learning by Extracting Transferable Robot Skills from Offline Data—0
Extracting Action Sequences from Texts Based on Deep Reinforcement Learning—0
Extracting Expert's Goals by What-if Interpretable Modeling—0
Extracting Latent State Representations with Linear Dynamics from Rich Observations—0
Extrapolation in Gridworld Markov-Decision Processes—0
Extreme Risk Mitigation in Reinforcement Learning using Extreme Value Theory—0
Extreme State Aggregation Beyond MDPs—0
Extreme Value Monte Carlo Tree Search—0
ExWarp: Extrapolation and Warping-based Temporal Supersampling for High-frequency Displays—0
Eye of the Beholder: Improved Relation Generalization for Text-based Reinforcement Learning Agents—0
Show:102550
← PrevPage 430 of 605Next →

Benchmark Results

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