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

Showing 60516100 of 15113 papers

TitleStatusHype
Learning Time-Invariant Reward Functions through Model-Based Inverse Reinforcement Learning0
Learning Time Reduction Using Warm Start Methods for a Reinforcement Learning Based Supervisory Control in Hybrid Electric Vehicle Applications0
Learning to act: a Reinforcement Learning approach to recommend the best next activities0
Learning to Act in Decentralized Partially Observable MDPs0
Learning to Activate Relay Nodes: Deep Reinforcement Learning Approach0
Learning to Advise and Learning from Advice in Cooperative Multi-Agent Reinforcement Learning0
Learning to Assign: Towards Fair Task Assignment in Large-Scale Ride Hailing0
Learning to Assist Agents by Observing Them0
Learning to be Safe: Deep RL with a Safety Critic0
Learning to Bridge the Gap: Efficient Novelty Recovery with Planning and Reinforcement Learning0
Learning to Centralize Dual-Arm Assembly0
Learning to Clarify by Reinforcement Learning Through Reward-Weighted Fine-Tuning0
Learning to Code: Coded Caching via Deep Reinforcement Learning0
Learning to Collaborate in Multi-Module Recommendation via Multi-Agent Reinforcement Learning without Communication0
Learning to Collaborate: Multi-Scenario Ranking via Multi-Agent Reinforcement Learning0
Learning to Combat Compounding-Error in Model-Based Reinforcement Learning0
Learning to Communicate: A Machine Learning Framework for Heterogeneous Multi-Agent Robotic Systems0
Learning to Communicate in Multi-Agent Reinforcement Learning : A Review0
Learning to communicate through imagination with model-based deep multi-agent reinforcement learning0
Learning to Communicate to Solve Riddles with Deep Distributed Recurrent Q-Networks0
Learning to Communicate with Intent: An Introduction0
Learning to Communicate with Reinforcement Learning for an Adaptive Traffic Control System0
Learning to Compose Hierarchical Object-Centric Controllers for Robotic Manipulation0
Learning to Compose Words into Sentences with Reinforcement Learning0
Learning to Control DC Motor for Micromobility in Real Time with Reinforcement Learning0
Learning to Control Visual Abstractions for Structured Exploration in Deep Reinforcement Learning0
Learning to Cooperate via Policy Search0
Learning to Cooperate with Unseen Agent via Meta-Reinforcement Learning0
Learning to Coordinate Multiple Reinforcement Learning Agents for Diverse Query Reformulation0
Learning to Decompose Compound Questions with Reinforcement Learning0
Learning to Design Games: Strategic Environments in Reinforcement Learning0
Learning to Diagnose: Assimilating Clinical Narratives using Deep Reinforcement Learning0
Learning to Discuss Strategically: A Case Study on One Night Ultimate Werewolf0
Learning to Drive using Inverse Reinforcement Learning and Deep Q-Networks0
Learning to Drive Using Sparse Imitation Reinforcement Learning0
Learning to Dynamically Select Between Reward Shaping Signals0
Learning to Explore a Class of Multiple Reward-Free Environments0
Learning to Explore Multiple Environments without Rewards0
Learning to Explore via Meta-Policy Gradient0
Learning to Explore with Meta-Policy Gradient0
Learning to Explore with Pleasure0
Learning to Extract Coherent Summary via Deep Reinforcement Learning0
Learning to Factor Policies and Action-Value Functions: Factored Action Space Representations for Deep Reinforcement learning0
Learning to Forecast Aleatoric and Epistemic Uncertainties over Long Horizon Trajectories0
Learning to Generate All Feasible Actions0
Learning to generate Reliable Broadcast Algorithms0
Learning to Generate Research Idea with Dynamic Control0
Learning to Generate Structured Queries from Natural Language with Indirect Supervision0
Learning to Grasp from 2.5D images: a Deep Reinforcement Learning Approach0
Learning to Grasp on the Moon from 3D Octree Observations with Deep Reinforcement Learning0
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Benchmark Results

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