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 4061–4070 of 15113 papers

TitleStatusHype
Acquiring Diverse Skills using Curriculum Reinforcement Learning with Mixture of Experts—0
Acquiring Target Stacking Skills by Goal-Parameterized Deep Reinforcement Learning—0
ACTER: Diverse and Actionable Counterfactual Sequences for Explaining and Diagnosing RL Policies—0
Acting upon Imagination: when to trust imagined trajectories in model based reinforcement learning—0
Actionable Models: Unsupervised Offline Reinforcement Learning of Robotic Skills—0
Action abstractions for amortized sampling—0
Action and Trajectory Planning for Urban Autonomous Driving with Hierarchical Reinforcement Learning—0
Action Categorization for Computationally Improved Task Learning and Planning—0
Action-Constrained Reinforcement Learning for Frame-Level Bit Allocation in HEVC/H.265 through Frank-Wolfe Policy Optimization—0
Action-dependent Control Variates for Policy Optimization via Stein Identity—0
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Benchmark Results

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