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

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Showing 5251–5275 of 15113 papers

TitleStatusHype
Implementations that Matter in Cooperative Multi-Agent Reinforcement Learning—0
Implementing Online Reinforcement Learning with Temporal Neural Networks—0
Implementing Reinforcement Learning Algorithms in Retail Supply Chains with OpenAI Gym Toolkit—0
Implications of Human Irrationality for Reinforcement Learning—0
Implicitly Regularized RL with Implicit Q-Values—0
Implicit Neural-Representation Learning for Elastic Deformable-Object Manipulations—0
Implicit Offline Reinforcement Learning via Supervised Learning—0
Implicit Policy for Reinforcement Learning—0
Importance mixing: Improving sample reuse in evolutionary policy search methods—0
Importance of Empirical Sample Complexity Analysis for Offline Reinforcement Learning—0
Importance of Environment Design in Reinforcement Learning: A Study of a Robotic Environment—0
Importance Sampling-Guided Meta-Training for Intelligent Agents in Highly Interactive Environments—0
Importance Weighted Evolution Strategies—0
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
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Benchmark Results

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