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 40514100 of 15113 papers

TitleStatusHype
A Constrained-Optimization Approach to the Execution of Prioritized Stacks of Learned Multi-Robot Tasks0
A Contact-Safe Reinforcement Learning Framework for Contact-Rich Robot Manipulation0
A Contextual-bandit-based Approach for Informed Decision-making in Clinical Trials0
A Contextualized Real-Time Multimodal Emotion Recognition for Conversational Agents using Graph Convolutional Networks in Reinforcement Learning0
A Contraction Approach to Model-based Reinforcement Learning0
A General Markov Decision Process Framework for Directly Learning Optimal Control Policies0
A Convergent Variant of the Boltzmann Softmax Operator in Reinforcement Learning0
A Convex Programming Approach to Data-Driven Risk-Averse Reinforcement Learning0
A cooperative game for automated learning of elasto-plasticity knowledge graphs and models with AI-guided experimentation0
A Cooperative Reinforcement Learning Environment for Detecting and Penalizing Betrayal0
Acquiring Diverse Skills using Curriculum Reinforcement Learning with Mixture of Experts0
Acquiring Target Stacking Skills by Goal-Parameterized Deep Reinforcement Learning0
ACTER: Diverse and Actionable Counterfactual Sequences for Explaining and Diagnosing RL Policies0
Acting upon Imagination: when to trust imagined trajectories in model based reinforcement learning0
Actionable Models: Unsupervised Offline Reinforcement Learning of Robotic Skills0
Action abstractions for amortized sampling0
Action and Trajectory Planning for Urban Autonomous Driving with Hierarchical Reinforcement Learning0
Action Categorization for Computationally Improved Task Learning and Planning0
Action-Constrained Reinforcement Learning for Frame-Level Bit Allocation in HEVC/H.265 through Frank-Wolfe Policy Optimization0
Action-dependent Control Variates for Policy Optimization via Stein Identity0
Action-Evolution Petri Nets: a Framework for Modeling and Solving Dynamic Task Assignment Problems0
Action-Gap Phenomenon in Reinforcement Learning0
Action Gaps and Advantages in Continuous-Time Distributional Reinforcement Learning0
Action Guidance with MCTS for Deep Reinforcement Learning0
Action-modulated midbrain dopamine activity arises from distributed control policies0
Action Noise in Off-Policy Deep Reinforcement Learning: Impact on Exploration and Performance0
Action-Quantized Offline Reinforcement Learning for Robotic Skill Learning0
Action Redundancy in Reinforcement Learning0
Action Set Based Policy Optimization for Safe Power Grid Management0
ActionSpotter: Deep Reinforcement Learning Framework for Temporal Action Spotting in Videos0
Actions Speak What You Want: Provably Sample-Efficient Reinforcement Learning of the Quantal Stackelberg Equilibrium from Strategic Feedbacks0
Active 6D Multi-Object Pose Estimation in Cluttered Scenarios with Deep Reinforcement Learning0
Active Alignments of Lens Systems with Reinforcement Learning0
Active Classification of Moving Targets with Learned Control Policies0
Active Coverage for PAC Reinforcement Learning0
Active Deep Q-learning with Demonstration0
Active Exploration in Bayesian Model-based Reinforcement Learning for Robot Manipulation0
Active Finite Reward Automaton Inference and Reinforcement Learning Using Queries and Counterexamples0
Active Hierarchical Imitation and Reinforcement Learning0
Active hypothesis testing in unknown environments using recurrent neural networks and model free reinforcement learning0
Active Inference as a Model of Agency0
Active Information Acquisition0
Active Learning for Risk-Sensitive Inverse Reinforcement Learning0
Active Learning of Causal Structures with Deep Reinforcement Learning0
Active Measure Reinforcement Learning for Observation Cost Minimization0
Active Perception Applied To Unmanned Aerial Vehicles Through Deep Reinforcement Learning0
Active Perception for Tactile Sensing: A Task-Agnostic Attention-Based Approach0
Active Perception in Adversarial Scenarios using Maximum Entropy Deep Reinforcement Learning0
Active Phase-Encode Selection for Slice-Specific Fast MR Scanning Using a Transformer-Based Deep Reinforcement Learning Framework0
Active Predicting Coding: Brain-Inspired Reinforcement Learning for Sparse Reward Robotic Control Problems0
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Benchmark Results

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