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 11526–11550 of 15113 papers

TitleStatusHype
Imitation Bootstrapped Reinforcement Learning—0
Imitation Is Not Enough: Robustifying Imitation with Reinforcement Learning for Challenging Driving Scenarios—0
Forward and inverse reinforcement learning sharing network weights and hyperparameters—0
Imitation Learning for Human Pose Prediction—0
Imitation Learning with Concurrent Actions in 3D Games—0
Imitation-Projected Programmatic Reinforcement Learning—0
Imitation with Neural Density Models—0
IMM: An Imitative Reinforcement Learning Approach with Predictive Representation Learning for Automatic Market Making—0
Imminent Collision Mitigation with Reinforcement Learning and Vision—0
IMPACT: Importance Weighted Asynchronous Architectures with Clipped Target Networks—0
Impact of Price Inflation on Algorithmic Collusion Through Reinforcement Learning Agents—0
Impedance Matching: Enabling an RL-Based Running Jump in a Quadruped Robot—0
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
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Benchmark Results

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