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 11551–11575 of 15113 papers

TitleStatusHype
Importance Weighted Policy Learning and Adaptation—0
Importance Weighted Transfer of Samples in Reinforcement Learning—0
Imposing Robust Structured Control Constraint on Reinforcement Learning of Linear Quadratic Regulator—0
Improper Reinforcement Learning with Gradient-based Policy Optimization—0
Improved Activity Forecasting for Generating Trajectories—0
Provably Improved Context-Based Offline Meta-RL with Attention and Contrastive Learning—0
Improved cooperation by balancing exploration and exploitation in intertemporal social dilemma tasks—0
Improved Corruption Robust Algorithms for Episodic Reinforcement Learning—0
Improved Exploration through Latent Trajectory Optimization in Deep Deterministic Policy Gradient—0
Improved Learning in Evolution Strategies via Sparser Inter-Agent Network Topologies—0
Improved Learning of Robot Manipulation Tasks via Tactile Intrinsic Motivation—0
Improved Memories Learning—0
Improved Sample Complexity for Stochastic Compositional Variance Reduced Gradient—0
Improved Regret for Differentially Private Exploration in Linear MDP—0
Improved Regret for Efficient Online Reinforcement Learning with Linear Function Approximation—0
Improved Reinforcement Learning through Imitation Learning Pretraining Towards Image-based Autonomous Driving—0
Improved Reinforcement Learning with Curriculum—0
Improved Robustness and Safety for Autonomous Vehicle Control with Adversarial Reinforcement Learning—0
Improved Sample Complexity for Reward-free Reinforcement Learning under Low-rank MDPs—0
Improved Worst-Case Regret Bounds for Randomized Least-Squares Value Iteration—0
Improvements on Hindsight Learning—0
Improving Actor-Critic Reinforcement Learning via Hamiltonian Monte Carlo Method—0
Improving generalization to new environments and removing catastrophic forgetting in Reinforcement Learning by using an eco-system of agents—0
Improving a Proportional Integral Controller with Reinforcement Learning on a Throttle Valve Benchmark—0
Improving a sequence-to-sequence nlp model using a reinforcement learning policy algorithm—0
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Benchmark Results

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