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 5801–5825 of 15113 papers

TitleStatusHype
Learn Goal-Conditioned Policy with Intrinsic Motivation for Deep Reinforcement Learning—0
Learning 3D Navigation Protocols on Touch Interfaces with Cooperative Multi-Agent Reinforcement Learning—0
Learning 6DoF Grasping Using Reward-Consistent Demonstration—0
Learning a Behavioral Repertoire from Demonstrations—0
Learning Abstract Models for Strategic Exploration and Fast Reward Transfer—0
The Pitfall of More Powerful Autoencoders in Lidar-Based Navigation—0
Learning a Contact-Adaptive Controller for Robust, Efficient Legged Locomotion—0
Learning Actionable Representations with Goal Conditioned Policies—0
Learning Action Representations for Reinforcement Learning—0
Learning Action Translator for Meta Reinforcement Learning on Sparse-Reward Tasks—0
Learning Active Learning in the Batch-Mode Setup with Ensembles of Active Learning Agents—0
Meta-Adversarial Inverse Reinforcement Learning for Decision-making Tasks—0
Learning Adaptive Dexterous Grasping from Single Demonstrations—0
Learning agents with prioritization and parameter noise in continuous state and action space—0
Learning Agile Locomotion via Adversarial Training—0
Learning Agile Skills via Adversarial Imitation of Rough Partial Demonstrations—0
Learning-Aided Heuristics Design for Storage System—0
Learning Algorithmic Solutions to Symbolic Planning Tasks with a Neural Computer Architecture—0
Learning Algorithmic Solutions to Symbolic Planning Tasks with a Neural Computer—0
Learning Algorithms for Intelligent Agents and Mechanisms—0
Learning Algorithms for Regenerative Stopping Problems with Applications to Shipping Consolidation in Logistics—0
Learning a local trading strategy: deep reinforcement learning for grid-scale renewable energy integration—0
Learning Altruistic Behaviours in Reinforcement Learning without External Rewards—0
Learning and Adapting Agile Locomotion Skills by Transferring Experience—0
Learning and Exploiting Multiple Subgoals for Fast Exploration in Hierarchical Reinforcement Learning—0
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Benchmark Results

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