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 6001–6025 of 15113 papers

TitleStatusHype
Learning Real-World Robot Policies by Dreaming—0
Learning Relative Return Policies With Upside-Down Reinforcement Learning—0
Learning Representations in Model-Free Hierarchical Reinforcement Learning—0
Learning Representations in Reinforcement Learning: an Information Bottleneck Approach—0
Learning Retrospective Knowledge with Reverse Reinforcement Learning—0
Learning Reusable Options for Multi-Task Reinforcement Learning—0
Learning Reward Machines: A Study in Partially Observable Reinforcement Learning—0
Learning Rewards to Optimize Global Performance Metrics in Deep Reinforcement Learning—0
Learning Robotic Assembly from CAD—0
Learning Robotic Manipulation Skills Using an Adaptive Force-Impedance Action Space—0
Learning Robot Soccer from Egocentric Vision with Deep Reinforcement Learning—0
Learning Robust Autonomous Navigation and Locomotion for Wheeled-Legged Robots—0
Learning Robust Controllers Via Probabilistic Model-Based Policy Search—0
Learning Robust Rewards with Adverserial Inverse Reinforcement Learning—0
Learning Routines for Effective Off-Policy Reinforcement Learning—0
Learning Runtime Parameters in Computer Systems with Delayed Experience Injection—0
Learning Safe Policies with Cost-sensitive Advantage Estimation—0
Learning Safe Policies with Expert Guidance—0
Learning safety critics via a non-contractive binary bellman operator—0
Learning Sampling Policy for Faster Derivative Free Optimization—0
Learning Security Strategies through Game Play and Optimal Stopping—0
Learning Self-Game-Play Agents for Combinatorial Optimization Problems—0
Learning Shaping Strategies in Human-in-the-loop Interactive Reinforcement Learning—0
Learning Shared Representations in Multi-task Reinforcement Learning—0
Learning Soft Driving Constraints from Vectorized Scene Embeddings while Imitating Expert Trajectories—0
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Benchmark Results

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