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 6076–6100 of 15113 papers

TitleStatusHype
Learning to Control Visual Abstractions for Structured Exploration in Deep Reinforcement Learning—0
Learning to Cooperate via Policy Search—0
Learning to Cooperate with Unseen Agent via Meta-Reinforcement Learning—0
Learning to Coordinate Multiple Reinforcement Learning Agents for Diverse Query Reformulation—0
Learning to Decompose Compound Questions with Reinforcement Learning—0
Learning to Design Games: Strategic Environments in Reinforcement Learning—0
Learning to Diagnose: Assimilating Clinical Narratives using Deep Reinforcement Learning—0
Learning to Discuss Strategically: A Case Study on One Night Ultimate Werewolf—0
Learning to Drive using Inverse Reinforcement Learning and Deep Q-Networks—0
Learning to Drive Using Sparse Imitation Reinforcement Learning—0
Learning to Dynamically Select Between Reward Shaping Signals—0
Learning to Explore a Class of Multiple Reward-Free Environments—0
Learning to Explore Multiple Environments without Rewards—0
Learning to Explore via Meta-Policy Gradient—0
Learning to Explore with Meta-Policy Gradient—0
Learning to Explore with Pleasure—0
Learning to Extract Coherent Summary via Deep Reinforcement Learning—0
Learning to Factor Policies and Action-Value Functions: Factored Action Space Representations for Deep Reinforcement learning—0
Learning to Forecast Aleatoric and Epistemic Uncertainties over Long Horizon Trajectories—0
Learning to Generate All Feasible Actions—0
Learning to generate Reliable Broadcast Algorithms—0
Learning to Generate Research Idea with Dynamic Control—0
Learning to Generate Structured Queries from Natural Language with Indirect Supervision—0
Learning to Grasp from 2.5D images: a Deep Reinforcement Learning Approach—0
Learning to Grasp on the Moon from 3D Octree Observations with Deep Reinforcement Learning—0
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Benchmark Results

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