Codes for "Learning from Sparse Offline Datasets via Conservative Density Estimation"
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Updated
Sep 27, 2023 - Python
Codes for "Learning from Sparse Offline Datasets via Conservative Density Estimation"
The easiest way to copy your flight log files and videos from racing drones and goggles DVR.
Implementation of Fisher_BRC in "Offline Reinforcement Learning with Fisher Divergence Critic Regularization" based on BRAC family.
Codebase for the paper "Efficient Offline Reinforcement Learning: The Critic is Critical"
Offline Reinforcement Learning Framework in JAX
Pytorch based reimplementation of COMS: Conservative Objective Models for Effective Offline Model-Based Optimization.
Non-modular implementation of common RL algorithms
Large-Scale and Comprehensive Data Hub for Reinforcement Learning
The source code to Cross-Validated Off-Policy Evaluation
Python code to implement hard sampling based task representation learning for robust offline meta RL
Benchmark for "Offline Policy Comparison with Confidence"
Implementation of Offline Reinforcement Learning in Gym Mini-Grid Environment 🔑
"S2P: State-conditioned Image Synthesis for Data Augmentation in Offline Reinforcement Learning" (NeurIPS 2022)
Robust Offline Reinforcement Learning with Heavy-Tailed Rewards
Original implementations of the VC-FB and MC-FB algorithms from "Zero-Shot Reinforcement Learning from Low Quality Data" by Jeen et. al (2024).
Implementation of CQL in "Conservative Q-Learning for Offline Reinforcement Learning" based on BRAC family.
[AAAI 2022] The official implementation of CPQ in "Constraints Penalized Q-learning for Safe Offline Reinforcement Learning"
Pytorch implementation of BEAR in "Stabilizing Off-Policy Q-Learning via Bootstrapping Error Reduction"
Official implementation for "Let Offline RL Flow: Training Conservative Agents in the Latent Space of Normalizing Flows", NeurIPS 2022, Offline RL Workshop
[AAAI 2022] The official implementation of "DeepThermal: Combustion Optimization for Thermal Power Generating Units Using Offline Reinforcement Learning"
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