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 4051–4075 of 15113 papers

TitleStatusHype
A Constrained-Optimization Approach to the Execution of Prioritized Stacks of Learned Multi-Robot Tasks—0
A Contact-Safe Reinforcement Learning Framework for Contact-Rich Robot Manipulation—0
A Contextual-bandit-based Approach for Informed Decision-making in Clinical Trials—0
A Contextualized Real-Time Multimodal Emotion Recognition for Conversational Agents using Graph Convolutional Networks in Reinforcement Learning—0
A Contraction Approach to Model-based Reinforcement Learning—0
A General Markov Decision Process Framework for Directly Learning Optimal Control Policies—0
A Convergent Variant of the Boltzmann Softmax Operator in Reinforcement Learning—0
A Convex Programming Approach to Data-Driven Risk-Averse Reinforcement Learning—0
A cooperative game for automated learning of elasto-plasticity knowledge graphs and models with AI-guided experimentation—0
A Cooperative Reinforcement Learning Environment for Detecting and Penalizing Betrayal—0
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
Action-Evolution Petri Nets: a Framework for Modeling and Solving Dynamic Task Assignment Problems—0
Action-Gap Phenomenon in Reinforcement Learning—0
Action Gaps and Advantages in Continuous-Time Distributional Reinforcement Learning—0
Action Guidance with MCTS for Deep Reinforcement Learning—0
Action-modulated midbrain dopamine activity arises from distributed control policies—0
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Benchmark Results

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