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 6001–6050 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
Learning Sparse Representations in Reinforcement Learning with Sparse Coding—0
Learning sparse representations in reinforcement learning—0
Learning Sparse Representations Incrementally in Deep Reinforcement Learning—0
Learning State Representations for Query Optimization with Deep Reinforcement Learning—0
Learning State Representations in Complex Systems with Multimodal Data—0
Learning State Representations via Temporal Cycle-Consistency Constraint in Model-Based Reinforcement Learning—0
Learning Strategic Language Agents in the Werewolf Game with Iterative Latent Space Policy Optimization—0
Learning Structured Communication for Multi-agent Reinforcement Learning—0
Learning swimming escape patterns for larval fish under energy constraints—0
Learning Symbolic Representations for Reinforcement Learning of Non-Markovian Behavior—0
Learning Symbolic Rules for Interpretable Deep Reinforcement Learning—0
Learning Task Automata for Reinforcement Learning using Hidden Markov Models—0
Learning Task-Driven Control Policies via Information Bottlenecks—0
Learning Task Informed Abstractions—0
Learning Task-Relevant Representations with Selective Contrast for Reinforcement Learning in a Real-World Application—0
Learning Task Sampling Policy for Multitask Learning—0
Learning Temporal Abstraction with Information-theoretic Constraints for Hierarchical Reinforcement Learning—0
Learning Temporally Extended Skills in Continuous Domains as Symbolic Actions for Planning—0
Learning Temporal Point Processes via Reinforcement Learning—0
Learning the Arrow of Time for Problems in Reinforcement Learning—0
Learning the Extraction Order of Multiple Relational Facts in a Sentence with Reinforcement Learning—0
Learning the model-free linear quadratic regulator via random search—0
Learning the policy for mixed electric platoon control of automated and human-driven vehicles at signalized intersection: a random search approach—0
Learning the Target Network in Function Space—0
Learning through Probing: a decentralized reinforcement learning architecture for social dilemmas—0
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Benchmark Results

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