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 10426–10450 of 15113 papers

TitleStatusHype
On Connections between Constrained Optimization and Reinforcement Learning—0
On Convergence of Average-Reward Q-Learning in Weakly Communicating Markov Decision Processes—0
On Convergence Rate of Adaptive Multiscale Value Function Approximation For Reinforcement Learning—0
On Corruption-Robustness in Performative Reinforcement Learning—0
On Covariate Shift of Latent Confounders in Imitation and Reinforcement Learning—0
On Decentralizing Federated Reinforcement Learning in Multi-Robot Scenarios—0
On Double Descent in Reinforcement Learning with LSTD and Random Features—0
On Dynamic Programming Decompositions of Static Risk Measures in Markov Decision Processes—0
On Efficiency in Hierarchical Reinforcement Learning—0
On Enhancing Network Throughput using Reinforcement Learning in Sliced Testbeds—0
One Policy but Many Worlds: A Scalable Unified Policy for Versatile Humanoid Locomotion—0
One Policy is Enough: Parallel Exploration with a Single Policy is Near-Optimal for Reward-Free Reinforcement Learning—0
One RL to See Them All: Visual Triple Unified Reinforcement Learning—0
One-shot learning and behavioral eligibility traces in sequential decision making—0
One-Shot Learning of Manipulation Skills with Online Dynamics Adaptation and Neural Network Priors—0
One-shot, Offline and Production-Scalable PID Optimisation with Deep Reinforcement Learning—0
One-Step Distributional Reinforcement Learning—0
Sample Complexity of Offline Reinforcement Learning with Deep ReLU Networks—0
On Gap-dependent Bounds for Offline Reinforcement Learning—0
On Generalization and Distributional Update for Mimicking Observations with Adequate Exploration—0
On Hard Exploration for Reinforcement Learning: a Case Study in Pommerman—0
On Improving Cross-dataset Generalization of Deepfake Detectors—0
On Improving Deep Reinforcement Learning for POMDPs—0
On Inductive Biases in Deep Reinforcement Learning—0
On Information Asymmetry in Competitive Multi-Agent Reinforcement Learning: Convergence and Optimality—0
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Benchmark Results

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