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 6751–6775 of 15113 papers

TitleStatusHype
Beyond Value: CHECKLIST for Testing Inferences in Planning-Based RL—0
Reward Poisoning Attacks on Offline Multi-Agent Reinforcement Learning—0
MACC: Cross-Layer Multi-Agent Congestion Control with Deep Reinforcement Learning—0
Reinforcement Learning with Neural Radiance Fields—0
Offline Reinforcement Learning with Causal Structured World Models—0
Disentangling Epistemic and Aleatoric Uncertainty in Reinforcement Learning—0
A Deep Reinforcement Learning Framework For Column GenerationCode0
Joint Energy Dispatch and Unit Commitment in Microgrids Based on Deep Reinforcement Learning—0
KCRL: Krasovskii-Constrained Reinforcement Learning with Guaranteed Stability in Nonlinear Dynamical Systems—0
Equivariant Reinforcement Learning for Quadrotor UAV—0
Incorporating Explicit Uncertainty Estimates into Deep Offline Reinforcement Learning—0
Incrementality Bidding via Reinforcement Learning under Mixed and Delayed Rewards—0
HEX: Human-in-the-loop Explainability via Deep Reinforcement Learning—0
Sample-Efficient Reinforcement Learning of Partially Observable Markov Games—0
Policy Gradient Algorithms with Monte Carlo Tree Learning for Non-Markov Decision Processes—0
Posterior Coreset Construction with Kernelized Stein Discrepancy for Model-Based Reinforcement Learning—0
RACA: Relation-Aware Credit Assignment for Ad-Hoc Cooperation in Multi-Agent Deep Reinforcement Learning—0
Reinforcement learning based parameters adaption method for particle swarm optimization—0
Offline Reinforcement Learning with Differential Privacy—0
RLSS: A Deep Reinforcement Learning Algorithm for Sequential Scene Generation—0
On Gap-dependent Bounds for Offline Reinforcement Learning—0
Predecessor Features—0
Model Generation with Provable Coverability for Offline Reinforcement Learning—0
Provably Efficient Offline Multi-agent Reinforcement Learning via Strategy-wise Bonus—0
Provably Efficient Lifelong Reinforcement Learning with Linear Function Approximation—0
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Benchmark Results

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