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 8351–8375 of 15113 papers

TitleStatusHype
Omega-Regular Reward Machines—0
OMG-RL:Offline Model-based Guided Reward Learning for Heparin Treatment—0
OmniDRL: Robust Pedestrian Detection using Deep Reinforcement Learning on Omnidirectional Cameras—0
OmniRL: In-Context Reinforcement Learning by Large-Scale Meta-Training in Randomized Worlds—0
On- and Off-Policy Monotonic Policy Improvement—0
On Applications of Bootstrap in Continuous Space Reinforcement Learning—0
On Assessing The Safety of Reinforcement Learning algorithms Using Formal Methods—0
On Bellman equations for continuous-time policy evaluation I: discretization and approximation—0
On Bellman's principle of optimality and Reinforcement learning for safety-constrained Markov decision process—0
On-board Deep Q-Network for UAV-assisted Online Power Transfer and Data Collection—0
On Computation and Generalization of Generative Adversarial Imitation Learning—0
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
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Benchmark Results

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