deep rl

Value-Based Deep RL Scales Predictably ICML 2025 Distributional Reinforcement Learning with Regularized Wasserstein Loss NeurIPS 2024 Enhancing Robustness in Deep Reinforcement Learning: A Lyapunov Exponent Approach NeurIPS 2024 Exploiting the Replay Memory Before Exploring the Environment: Enhancing Reinforcement Learning Through Empirical MDP Iteration NeurIPS 2024 MetaRLEC: Meta-Reinforcement Learning for Discovery of Brain Effective Connectivity AAAI 2024 QGym: Scalable Simulation and Benchmarking of Queuing Network Controllers NeurIPS 2024 LESSON: Learning to Integrate Exploration Strategies for Reinforcement Learning via an Option Framework ICML 2023 Planning Multiple Epidemic Interventions with Reinforcement Learning IJCAI 2023 Safe Reinforcement Learning via Shielding under Partial Observability AAAI 2023 Vessel-to-Vessel Motion Compensation with Reinforcement Learning AAAI 2023 d3rlpy: An Offline Deep Reinforcement Learning Library JMLR 2022 PantheonRL: A MARL Library for Dynamic Training Interactions AAAI 2022 WarpDrive: Fast End-to-End Deep Multi-Agent Reinforcement Learning on a GPU JMLR 2022 A Minimalist Approach to Offline Reinforcement Learning NeurIPS 2021 Feynman-Kac Neural Network Architectures for Stochastic Control Using Second-Order FBSDE Theory L4DC 2020 Google Research Football: A Novel Reinforcement Learning Environment AAAI 2020 High Acceleration Reinforcement Learning for Real-World Juggling with Binary Rewards CoRL 2020 Making Efficient Use of Demonstrations to Solve Hard Exploration Problems ICLR 2020 Responsive Safety in Reinforcement Learning by PID Lagrangian Methods ICML 2020 SEED RL: Scalable and Efficient Deep-RL with Accelerated Central Inference ICLR 2020 DeepMDP: Learning Continuous Latent Space Models for Representation Learning ICML 2019