提前看287篇ICLR-2021 “强化学习”领域论文得分列表 - 深度强化学习实验室

提前看287篇ICLR-2021 “强化学习”领域论文得分列表

DeepRLearner

1. What Matters for On-Policy Deep Actor-Critic Methods? A Large-Scale Study, 8分

2. Invariant Representations for Reinforcement Learning without Reconstruction, 7.67分

3. Winning the L2RPN Challenge: Power Grid Management via Semi-Markov Afterstate Actor-Critic, 7.5分

4. Deep symbolic regression: Recovering mathematical expressions from data via risk-seeking policy gradients, 7.5分

5. Parrot: Data-Driven Behavioral Priors for Reinforcement Learning, 7.5分

6. Evolving Reinforcement Learning Algorithms, 7.33分

7. Global optimality of softmax policy gradient with single hidden layer neural networks in the mean-field regime, 7分

8. Single-Timescale Actor-Critic Provably Finds Globally Optimal Policy, 7分

9. UPDeT: Universal Multi-agent RL via Policy Decoupling with Transformers, 7分

10. Regularized Inverse Reinforcement Learning, 6.8分

11. Randomized Ensembled Double Q-Learning: Learning Fast Without a Model, 6.75分

12. Deployment-Efficient Reinforcement Learning via Model-Based Offline Optimization, 6.75分

13. Image Augmentation Is All You Need: Regularizing Deep Reinforcement Learning from Pixels, 6.75分

14. Support-set bottlenecks for video-text representation learning, 6.75分

15. A Sharp Analysis of Model-based Reinforcement Learning with Self-Play, 6.75分

16. RODE: Learning Roles to Decompose Multi-Agent Tasks, 6.67分

17. Text Generation by Learning from Off-Policy Demonstrations, 6.6分

18. Robust Reinforcement Learning on State Observations with Learned Optimal Adversary, 6.5分

19. Self-supervised Visual Reinforcement Learning with Object-centric Representations, 6.5分

20. On Effective Parallelization of Monte Carlo Tree Search, 6.5分

21. Non-asymptotic Confidence Intervals of Off-policy Evaluation: Primal and Dual Bounds , 6.5分

22. Efficient Transformers in Reinforcement Learning using Actor-Learner Distillation, 6.5分

23. Ask Your Humans: Using Human Instructions to Improve Generalization in Reinforcement Learning, 6.5分

24. SMiRL: Surprise Minimizing Reinforcement Learning in Unstable Environments, 6.5分

25. Model-Based Visual Planning with Self-Supervised Functional Distances, 6.5分

26. Learning-based Support Estimation in Sublinear Time, 6.5分

27. DOP: Off-Policy Multi-Agent Decomposed Policy Gradients, 6.5分

28. Correcting experience replay for multi-agent communication, 6.5分

29. Risk-Averse Offline Reinforcement Learning, 6.4分

30. Learning Value Functions in Deep Policy Gradients using Residual Variance, 6.33分

31. Contrastive Explanations for Reinforcement Learning via Embedded Self Predictions, 6.33分

32. PODS: Policy Optimization via Differentiable Simulation, 6.33分

33. Transient Non-stationarity and Generalisation in Deep Reinforcement Learning, 6.25分

34. Improving Learning to Branch via Reinforcement Learning, 6.25分

35. Mastering Atari with Discrete World Models, 6.25分

36. Data-Efficient Reinforcement Learning with Self-Predictive Representations, 6.25分

37. Local Information Opponent Modelling Using Variational Autoencoders, 6.25分

38. Contrastive Behavioral Similarity Embeddings for Generalization in Reinforcement Learning, 6.25分

39. Efficient Reinforcement Learning in Factored MDPs with Application to Constrained RL, 6.25分

40. Batch Reinforcement Learning Through Continuation Method, 6.25分

41. Optimistic Exploration with Backward Bootstrapped Bonus for Deep Reinforcement Learning, 6.2分

42. Optimism in Reinforcement Learning with Generalized Linear Function Approximation, 6分

43. Adversarially Guided Actor-Critic, 6分

44. QTRAN++: Improved Value Transformation for Cooperative Multi-Agent Reinforcement Learning, 6分

45. Policy Optimization in Zero-Sum Markov Games: Fictitious Self-Play Provably Attains Nash Equilibria, 6分

46. Optimistic Policy Optimization with General Function Approximations, 6分

47. Multi-Agent Collaboration via Reward Attribution Decomposition, 6分

48. Efficient Wasserstein Natural Gradients for Reinforcement Learning, 6分

49. Density Constrained Reinforcement Learning, 6分

50. Representation Balancing Offline Model-based Reinforcement Learning, 6分

51. Decoupling Representation Learning from Reinforcement Learning, 6分

52. Model-based micro-data reinforcement learning: what are the crucial model properties and which model to choose?, 5.8分

53. Model-based Asynchronous Hyperparameter and Neural Architecture Search, 5.8分

54. DeepAveragers: Offline Reinforcement Learning By Solving Derived Non-Parametric MDPs, 5.8分

55. Uncertainty Weighted Offline Reinforcement Learning, 5.8分

56. Optimizing Memory Placement using Evolutionary Graph Reinforcement Learning, 5.75分

57. Parameter-based Value Functions, 5.75分

58. Sample-Efficient Automated Deep Reinforcement Learning, 5.75分

59. Causal Inference Q-Network: Toward Resilient Reinforcement Learning, 5.75分

60. SACoD: Sensor Algorithm Co-Design Towards Efficient CNN-powered Intelligent PhlatCam, 5.75分

61. Learn Goal-Conditioned Policy with Intrinsic Motivation for Deep Reinforcement Learning, 5.75分

62. Benchmarks for Deep Off-Policy Evaluation, 5.75分

63. Shortest-Path Constrained Reinforcement Learning for Sparse Reward Tasks, 5.75分

64. Exploring Zero-Shot Emergent Communication in Embodied Multi-Agent Populations, 5.75分

65. Randomized Entity-wise Factorization for Multi-Agent Reinforcement Learning, 5.75分

66. Learning Robust State Abstractions for Hidden-Parameter Block MDPs, 5.75分

67. Adapting to Reward Progressivity via Spectral Reinforcement Learning, 5.75分

68. Accelerating Safe Reinforcement Learning with Constraint-mismatched Policies, 5.75分

69. Off-Dynamics Reinforcement Learning: Training for Transfer with Domain Classifiers, 5.75分

70. Meta-Reinforcement Learning With Informed Policy Regularization, 5.75分

71. Hierarchical Reinforcement Learning by Discovering Intrinsic Options, 5.75分

72. Multi-Agent Trust Region Learning, 5.75分

73. Unity of Opposites: SelfNorm and CrossNorm for Model Robustness, 5.75分

74. The Advantage Regret-Matching Actor-Critic, 5.67分

75. Differentiable Trust Region Layers for Deep Reinforcement Learning, 5.67分

76. Linear Representation Meta-Reinforcement Learning for Instant Adaptation, 5.67分

77. Symmetry-Aware Actor-Critic for 3D Molecular Design, 5.67分

78. The Importance of Pessimism in Fixed-Dataset Policy Optimization, 5.67分

79. Understanding and Leveraging Causal Relations in Deep Reinforcement Learning, 5.67分

80. Efficient Fully-Offline Meta-Reinforcement Learning via Distance Metric Learning and Behavior Regularization, 5.67分

81. Grounding Language to Entities for Generalization in Reinforcement Learning, 5.6分

82. Large Batch Simulation for Deep Reinforcement Learning, 5.6分

83. Deep Reinforcement Learning For Wireless Scheduling with Multiclass Services, 5.5分

84. Monotonic Robust Policy Optimization with Model Discrepancy, 5.5分

85. Truly Deterministic Policy Optimization, 5.5分

86. Distributional Reinforcement Learning for Risk-Sensitive Policies, 5.5分

87. Bounded Myopic Adversaries for Deep Reinforcement Learning Agents, 5.5分

88. Decoupling Exploration and Exploitation for Meta-Reinforcement Learning without Sacrifices, 5.5分

89. Discovering Diverse Multi-Agent Strategic Behavior via Reward Randomization, 5.5分

90. Blending MPC & Value Function Approximation for Efficient Reinforcement Learning, 5.5分

91. A Policy Gradient Algorithm for Learning to Learn in Multiagent Reinforcement Learning, 5.5分

92. The act of remembering: A study in partially observable reinforcement learning, 5.5分

93. Random Coordinate Langevin Monte Carlo, 5.5分

94. Provable Rich Observation Reinforcement Learning with Combinatorial Latent States, 5.5分

95. Automatic Data Augmentation for Generalization in Reinforcement Learning, 5.5分

96. Reinforcement Learning with Random Delays, 5.5分

97. On Proximal Policy Optimization's Heavy-Tailed Gradients , 5.5分

98. A Primal Approach to Constrained Policy Optimization: Global Optimality and Finite-Time Analysis, 5.5分

99. Regularization Matters in Policy Optimization - An Empirical Study on Continuous Control, 5.5分

100. Divide-and-Conquer Monte Carlo Tree Search, 5.5分

101. Status-Quo Policy Gradient in Multi-agent Reinforcement Learning, 5.5分

102. QPLEX: Duplex Dueling Multi-Agent Q-Learning, 5.5分

103. A Reduction Approach to Constrained Reinforcement Learning, 5.5分

104. Compute- and Memory-Efficient Reinforcement Learning with Latent Experience Replay, 5.5分

105. On Trade-offs of Image Prediction in Visual Model-Based Reinforcement Learning, 5.5分

106. Towards Understanding Linear Value Decomposition in Cooperative Multi-Agent Q-Learning, 5.5分

107. Average Reward Reinforcement Learning with Monotonic Policy Improvement, 5.5分

108. FactoredRL: Leveraging Factored Graphs for Deep Reinforcement Learning, 5.5分

109. Implicit Under-Parameterization Inhibits Data-Efficient Deep Reinforcement Learning, 5.5分

110. Scalable Bayesian Inverse Reinforcement Learning by Auto-Encoding Reward, 5.5分

111. Model-Based Offline Planning, 5.5分

112. BRAC+: Going Deeper with Behavior Regularized Offline Reinforcement Learning, 5.5分

113. Learning to Share in Multi-Agent Reinforcement Learning, 5.4分

114. Explicit Pareto Front Optimization for Constrained Reinforcement Learning, 5.33分

115. Guided Exploration with Proximal Policy Optimization using a Single Demonstration, 5.33分

116. Unsupervised Active Pre-Training for Reinforcement Learning, 5.33分

117. RECONNAISSANCE FOR REINFORCEMENT LEARNING WITH SAFETY CONSTRAINTS, 5.33分

118. Daylight: Assessing Generalization Skills of Deep Reinforcement Learning Agents, 5.33分

119. Diversity Actor-Critic: Sample-Aware Entropy Regularization for Sample-Efficient Exploration, 5.33分

120. OPAL: Offline Primitive Discovery for Accelerating Offline Reinforcement Learning, 5.33分

121. A REINFORCEMENT LEARNING FRAMEWORK FOR TIME DEPENDENT CAUSAL EFFECTS EVALUATION IN A/B TESTING, 5.33分

122. PettingZoo: Gym for Multi-Agent Reinforcement Learning, 5.25分

123. Hippocampal representations emerge when training recurrent neural networks on a memory dependent maze navigation task, 5.25分

124. Data-efficient Hindsight Off-policy Option Learning, 5.25分

125. Attacking Few-Shot Classifiers with Adversarial Support Sets, 5.25分

126. Coverage as a Principle for Discovering Transferable Behavior in Reinforcement Learning, 5.25分

127. Reinforcement Learning for Control with Probabilistic Stability Guarantee, 5.25分

128. Efficient Reinforcement Learning in Resource Allocation Problems Through Permutation Invariant Multi-task Learning, 5.25分

129. Meta-Reinforcement Learning Robust to Distributional Shift via Model Identification and Experience Relabeling, 5.25分

130. Solving Compositional Reinforcement Learning Problems via Task Reduction, 5.25分

131. Emergent Road Rules In Multi-Agent Driving Environments, 5.25分

132. EMaQ: Expected-Max Q-Learning Operator for Simple Yet Effective Offline and Online RL, 5.25分

133. Double Q-learning: New Analysis and Sharper Finite-time Bound, 5.25分

134. Safety Verification of Model Based Reinforcement Learning Controllers, 5.25分

135. D3C: Reducing the Price of Anarchy in Multi-Agent Learning, 5.25分

136. Near-Optimal Regret Bounds for Model-Free RL in Non-Stationary Episodic MDPs, 5.25分

137. Communication in Multi-Agent Reinforcement Learning: Intention Sharing, 5.25分

138. On the role of planning in model-based deep reinforcement learning, 5.25分

139. Reinforcement Learning with Latent Flow, 5.25分

140. Iterative Amortized Policy Optimization, 5.25分

141. Unsupervised Task Clustering for Multi-Task Reinforcement Learning, 5.25分

142. Adaptive Multi-model Fusion Learning for Sparse-Reward Reinforcement Learning, 5.25分

143. ERMAS: Learning Policies Robust to Reality Gaps in Multi-Agent Simulations, 5.25分

144. A Distributional Perspective on Actor-Critic Framework, 5.25分

145. Robust Reinforcement Learning using Adversarial Populations, 5.25分

146. The Compact Support Neural Network, 5.25分

147. RMIX: Risk-Sensitive Multi-Agent Reinforcement Learning, 5.25分

148. Meta-Model-Based Meta-Policy Optimization, 5.25分

149. Decentralized Deterministic Multi-Agent Reinforcement Learning, 5.2分

150. Transfer among Agents: An Efficient Multiagent Transfer Learning Framework, 5.2分

151. Gradient-based tuning of Hamiltonian Monte Carlo hyperparameters, 5分

152. Combining Imitation and Reinforcement Learning with Free Energy Principle, 5分

153. Ordering-Based Causal Discovery with Reinforcement Learning, 5分

154. Universal Value Density Estimation for Imitation Learning and Goal-Conditioned Reinforcement Learning, 5分

155. The Emergence of Individuality in Multi-Agent Reinforcement Learning, 5分

156. Explore with Dynamic Map: Graph Structured Reinforcement Learning, 5分

157. Offline Meta-Reinforcement Learning with Advantage Weighting, 5分

158. Deep Q-Learning with Low Switching Cost, 5分

159. AWAC: Accelerating Online Reinforcement Learning with Offline Datasets, 5分

160. A Strong On-Policy Competitor To PPO, 5分

161. Control-Aware Representations for Model-based Reinforcement Learning, 5分

162. Formal Language Constrained Markov Decision Processes, 5分

163. Multi-Agent Imitation Learning with Copulas, 5分

164. Projected Latent Markov Chain Monte Carlo: Conditional Sampling of Normalizing Flows, 5分

165. Efficient Competitive Self-Play Policy Optimization, 5分

166. Offline Model-Based Optimization via Normalized Maximum Likelihood Estimation, 5分

167. Beyond Prioritized Replay: Sampling States in Model-Based RL via Simulated Priorities, 5分

168. Action Guidance: Getting the Best of Sparse Rewards and Shaped Rewards for Real-time Strategy Games, 5分

169. What About Taking Policy as Input of Value Function: Policy-extended Value Function Approximator, 5分

170. Optimizing Information Bottleneck in Reinforcement Learning: A Stein Variational Approach, 5分

171. On the Estimation Bias in Double Q-Learning, 5分

172. Entropic Risk-Sensitive Reinforcement Learning: A Meta Regret Framework with Function Approximation, 5分

173. Goal-Auxiliary Actor-Critic for 6D Robotic Grasping with Point Clouds, 5分

174. Policy Gradient with Expected Quadratic Utility Maximization: A New Mean-Variance Approach in Reinforcement Learning, 5分

175. D2RL: Deep Dense Architectures in Reinforcement Learning, 5分

176. Intention Propagation for Multi-agent Reinforcement Learning, 5分

177. SIM-GAN: Adversarial Calibration of Multi-Agent Market Simulators., 5分

178. Preventing Value Function Collapse in Ensemble Q-Learning by Maximizing Representation Diversity, 5分

179. REPAINT: Knowledge Transfer in Deep Actor-Critic Reinforcement Learning, 5分

180. Mixture of Step Returns in Bootstrapped DQN, 5分

181. PAC-Bayesian Randomized Value Function with Informative Prior, 4.8分

182. Learning Safe Multi-agent Control with Decentralized Neural Barrier Certificates, 4.8分

183. Maximum Reward Formulation In Reinforcement Learning, 4.8分

184. Model-Free Counterfactual Credit Assignment, 4.75分

185. Plan-Based Asymptotically Equivalent Reward Shaping, 4.75分

186. Design-Bench: Benchmarks for Data-Driven Offline Model-Based Optimization, 4.75分

187. Regioned Episodic Reinforcement Learning, 4.75分

188. Reinforcement Learning with Bayesian Classifiers: Efficient Skill Learning from Outcome Examples, 4.75分

189. Provably More Efficient Q-Learning in the One-Sided-Feedback/Full-Feedback Settings, 4.75分

190. Systematic Evaluation of Causal Discovery in Visual Model Based Reinforcement Learning, 4.75分

191. Safe Reinforcement Learning with Natural Language Constraints, 4.75分

192. ReaPER: Improving Sample Efficiency in Model-Based Latent Imagination, 4.75分

193. Coordinated Multi-Agent Exploration Using Shared Goals, 4.75分

194. Measuring and mitigating interference in reinforcement learning, 4.75分

195. Hamiltonian Q-Learning: Leveraging Importance-sampling for Data Efficient RL, 4.75分

196. A Maximum Mutual Information Framework for Multi-Agent Reinforcement Learning, 4.75分

197. Non-decreasing Quantile Function Network with Efficient Exploration for Distributional Reinforcement Learning, 4.75分

198. Constrained Reinforcement Learning With Learned Constraints, 4.75分

199. Efficient Exploration for Model-based Reinforcement Learning with Continuous States and Actions, 4.75分

200. Error Controlled Actor-Critic Method to Reinforcement Learning, 4.75分

201. Cross-State Self-Constraint for Feature Generalization in Deep Reinforcement Learning, 4.75分

202. Safety Aware Reinforcement Learning (SARL), 4.75分

203. UneVEn: Universal Value Exploration for Multi-Agent Reinforcement Learning, 4.75分

204. Interpretable Reinforcement Learning With Neural Symbolic Logic, 4.67分

205. Network Reusability Analysis for Multi-Joint Robot Reinforcement Learning, 4.67分

206. Factored Action Spaces in Deep Reinforcement Learning, 4.67分

207. Genetic Soft Updates for Policy Evolution in Deep Reinforcement Learning, 4.67分

208. The Skill-Action Architecture: Learning Abstract Action Embeddings for Reinforcement Learning, 4.67分

209. Learning Intrinsic Symbolic Rewards in Reinforcement Learning, 4.67分

210. Robust Offline Reinforcement Learning from Low-Quality Data, 4.6分

211. Adaptive Learning Rates for Multi-Agent Reinforcement Learning, 4.6分

212. Revisiting Parameter Sharing in Multi-Agent Deep Reinforcement Learning, 4.5分

213. Addressing Distribution Shift in Online Reinforcement Learning with Offline Datasets, 4.5分

214. TOMA: Topological Map Abstraction for Reinforcement Learning, 4.5分

215. Multi-agent Policy Optimization with Approximatively Synchronous Advantage Estimation, 4.5分

216. Why Convolutional Networks Learn Oriented Bandpass Filters: Theory and Empirical Support, 4.5分

217. Self-Activating Neural Ensembles for Continual Reinforcement Learning, 4.5分

218. Approximating Pareto Frontier through Bayesian-optimization-directed Robust Multi-objective Reinforcement Learning, 4.5分

219. Model-Based Reinforcement Learning via Latent-Space Collocation, 4.5分

220. CDT: Cascading Decision Trees for Explainable Reinforcement Learning, 4.5分

221. PGPS : Coupling Policy Gradient with Population-based Search, 4.5分

222. CAT-SAC: Soft Actor-Critic with Curiosity-Aware Entropy Temperature, 4.5分

223. Learning to Observe with Reinforcement Learning, 4.5分

224. Probabilistic Mixture-of-Experts for Efficient Deep Reinforcement Learning, 4.5分

225. Visual Imitation with Reinforcement Learning using Recurrent Siamese Networks, 4.5分

226. Lyapunov Barrier Policy Optimization, 4.5分

227. A Deeper Look at Discounting Mismatch in Actor-Critic Algorithms, 4.5分

228. Cross-Modal Domain Adaptation for Reinforcement Learning, 4.5分

229. L2E: Learning to Exploit Your Opponent, 4.5分

230. MQES: Max-Q Entropy Search for Efficient Exploration in Continuous Reinforcement Learning, 4.4分

231. Robust Multi-Agent Reinforcement Learning Driven by Correlated Equilibrium, 4.4分

232. R-LAtte: Attention Module for Visual Control via Reinforcement Learning, 4.33分

233. Multi-agent Deep FBSDE Representation For Large Scale Stochastic Differential Games, 4.33分

234. Aspect-based Sentiment Classification via Reinforcement Learning, 4.33分

235. Refine and Imitate: Reducing Repetition and Inconsistency in Dialogue Generation via Reinforcement Learning and Human Demonstration, 4.33分

236. An Examination of Preference-based Reinforcement Learning for Treatment Recommendation, 4.33分

237. Adaptive Dataset Sampling by Deep Policy Gradient, 4.33分

238. Convergence Proof for Actor-Critic Methods Applied to PPO and RUDDER, 4.25分

239. Q-Value Weighted Regression: Reinforcement Learning with Limited Data, 4.25分

240. ScheduleNet: Learn to Solve MinMax mTSP Using Reinforcement Learning with Delayed Reward, 4.25分

241. Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms, 4.25分

242. Reinforcement Learning for Sparse-Reward Object-Interaction Tasks in First-person Simulated 3D Environments, 4.25分

243. Model-Free Energy Distance for Pruning DNNs, 4.25分

244. D4RL: Datasets for Deep Data-Driven Reinforcement Learning, 4.25分

245. Exploring Transferability of Perturbations in Deep Reinforcement Learning, 4.25分

246. Alpha-DAG: a reinforcement learning based algorithm to learn Directed Acyclic Graphs, 4.25分

247. Visual Explanation using Attention Mechanism in Actor-Critic-based Deep Reinforcement Learning, 4.25分

248. Knapsack Pruning with Inner Distillation, 4.25分

249. Reinforcement Learning for Flexibility Design Problems, 4.25分

250. Model-based Navigation in Environments with Novel Layouts Using Abstract $2$-D Maps, 4.25分

251. Model-Based Robust Deep Learning: Generalizing to Natural, Out-of-Distribution Data, 4.25分

252. Structure and randomness in planning and reinforcement learning, 4.2分

253. Trust, but verify: model-based exploration in sparse reward environments, 4分

254. Play to Grade: Grading Interactive Coding Games as Classifying Markov Decision Process, 4分

255. Graph Convolutional Value Decomposition in Multi-Agent Reinforcement Learning, 4分

256. Regret Bounds and Reinforcement Learning Exploration of EXP-based Algorithms, 4分

257. MDP Playground: Controlling Dimensions of Hardness in Reinforcement Learning, 4分

258. Intrinsically Guided Exploration in Meta Reinforcement Learning, 4分

259. Adaptive N-step Bootstrapping with Off-policy Data, 4分

260. FORK: A FORward-looKing Actor for Model-Free Reinforcement Learning, 4分

261. Measuring Progress in Deep Reinforcement Learning Sample Efficiency , 4分

262. Advantage-Weighted Regression: Simple and Scalable Off-Policy Reinforcement Learning, 4分

263. Joint State-Action Embedding for Efficient Reinforcement Learning, 3.8分

264. Deep Reinforcement Learning for Optimal Stopping with Application in Financial Engineering, 3.75分

265. Playing Atari with Capsule Networks: A systematic comparison of CNN and CapsNets-based agents., 3.75分

266. Robust Constrained Reinforcement Learning for Continuous Control with Model Misspecification, 3.75分

267. Decorrelated Double Q-learning, 3.75分

268. Learning to Dynamically Select Between Reward Shaping Signals, 3.75分

269. Empirically Verifying Hypotheses Using Reinforcement Learning, 3.75分

270. Self-Supervised Continuous Control without Policy Gradient, 3.75分

271. Dynamic Relational Inference in Multi-Agent Trajectories, 3.75分

272. Greedy Multi-Step Off-Policy Reinforcement Learning, 3.75分

273. Addressing Extrapolation Error in Deep Offline Reinforcement Learning, 3.67分

274. Offline Policy Optimization with Variance Regularization, 3.67分

275. Fine-Tuning Offline Reinforcement Learning with Model-Based Policy Optimization, 3.6分

276. Learning to communicate through imagination with model-based deep multi-agent reinforcement learning, 3.5分

277. A Robust Fuel Optimization Strategy For Hybrid Electric Vehicles: A Deep Reinforcement Learning Based Continuous Time Design Approach, 3.5分

278. Deep Reinforcement Learning With Adaptive Combined Critics, 3.5分

279. FSV: Learning to Factorize Soft Value Function for Cooperative Multi-Agent Reinforcement Learning, 3.4分

280. Success-Rate Targeted Reinforcement Learning by Disorientation Penalty, 3.25分

281. Explainable Reinforcement Learning Through Goal-Based Explanations, 3.25分

282. Hierarchical Meta Reinforcement Learning for Multi-Task Environments, 3.25分

283. Interpretable Meta-Reinforcement Learning with Actor-Critic Method, 3.2分

284. Reinforcement Learning Based Asymmetrical DNN Modularization for Optimal Loading, 3分

285. Stochastic Inverse Reinforcement Learning , 2.8分

286. Using Deep Reinforcement Learning to Train and Evaluate Instructional Sequencing Policies for an Intelligent Tutoring System, 2.67分

287. Guiding Representation Learning in Deep Generative Models with Policy Gradients, 2.5分

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