Deep Reinforcement Learning codes for study. Currently, there are only codes for algorithms: DQN, C51, QR-DQN, IQN, QUOTA.
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Mar 15, 2023 - Python
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distributional-rl
Here are 27 public repositories matching this topic...
[ICRA 2024] Decentralized Multi-Robot Navigation for Autonomous Surface Vehicles with Distributional Reinforcement Learning
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Aug 9, 2024 - Python
[IROS 2023] Robust Unmanned Surface Vehicle Navigation with Distributional Reinforcement Learning
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Oct 13, 2025 - Python
🐳 Implementation of various Distributional Reinforcement Learning Algorithms using TensorFlow2.
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Feb 28, 2021 - Python
PyTorch implementation of the state-of-the-art distributional reinforcement learning algorithm Fully Parameterized Quantile Function (FQF) and Extensions: N-step Bootstrapping, PER, Noisy Layer, Dueling Networks, and parallelization.
reinforcement-learning multiprocessing pytorch multi-environment reinforcement-learning-algorithms parallel-processing dueling-network-architecture prioritized-experience-replay quantile-functions dqn-pytorch distributional-rl noisy-layer fqf nstep-bootstrapping
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Oct 10, 2020 - Jupyter Notebook
Implementation of some of the Deep Distributional Reinforcement Learning Algorithms.
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Jun 17, 2025 - Python
[RA-L 2025] Distributional Reinforcement Learning Based Integrated Decision Making and Control for Autonomous Surface Vehicles
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Feb 4, 2026 - C++
PyTorch implementation of D4PG with the SOTA IQN Critic instead of C51. Implementation includes also the extensions Munchausen RL and D2RL which can be added to D4PG to improve its performance.
reinforcement-learning ddpg deep-deterministic-policy-gradient iqn prioritized-experience-replay actor-critic-algorithm pytorch-implementation distributional-rl d4pg munchausen-reinforcement-learning nstep-bootstrapping d2rl
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Apr 7, 2021 - Python
[UR 2023] Robust Route Planning with Distributional Reinforcement Learning in a Stochastic Road Network Environment
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Jun 19, 2024 - Python
Deep reinforcement learning framework for fast prototyping based on PyTorch
python machine-learning reinforcement-learning deep-learning deep-reinforcement-learning pytorch ddpg vtrace sac gymnasium actor-critic trpo ppo a2c acktr td3 retrace distributional-rl ray-tune
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Mar 12, 2023 - Python
DRL-Router is a method based on distributional reinforcement learning for RSP problem。
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Oct 11, 2021 - Python
Reinforcement learning algorithm implementation
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Oct 31, 2021 - Python
Distributional reinforcement learning algorithm using conjugated discrete distributions (C2D). Data and graphs for stochastic Atari 2600 environments.
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Jan 11, 2022 - Python
Author implementation of DSUP(q) algorithms from the NeurIPS 2024 paper "Action Gaps and Advantages in Continuous-Time Distributional Reinforcement Learning"
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Nov 15, 2024 - Python
Quantile Regression DQN implementation for bridge fleet maintenance optimization using Markov Decision Process. Migrated from C51 distributional RL (v0.8) with 200 quantiles and Huber loss. Features: Dueling architecture, Noisy Networks, PER, N-step learning. All 6 maintenance actions show positive returns with 68-78% VaR improvement.
reinforcement-learning deep-q-learning dueling-dqn prioritized-experience-replay noisy-networks structural-health-monitoring qr-dqn markov-decision-process distributional-rl condition-based-maintenance bridge-maintenance disaster-resilience infrastructure-maintenance-management n-step-learning bridge-fleet
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Dec 12, 2025 - Python
Solving CartPole using Distributional RL
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Feb 10, 2021 - Jupyter Notebook
Slide presentation reviewing advances in reinforcement learning
reinforcement-learning deep-reinforcement-learning q-learning deep-q-network deep-q-learning dueling-dqn dueling-q-networks prioritized-experience-replay multi-step-learning noisy-networks double-q-learning distributional-rl distributional-reinforcement-learning
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Aug 29, 2021
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Aug 31, 2019 - Python
C51 Distributional DQN (v0.8) for bridge fleet maintenance optimization. Implements categorical return distributions (Bellemare et al., PMLR 2017) with 300x speedup via vectorized projection. Combines Noisy Networks, Dueling DQN, Double DQN, PER, and n-step learning. Validated on 200-bridge fleet: +3,173 reward in 83 min (25k episodes).
reinforcement-learning deep-q-learning double-dqn dueling-dqn predictive-maintenance cbm prioritized-experience-replay noisy-networks large-scale-optimization structural-health-monitoring markov-decision-process distributional-rl prognostics-health-management bridge-maintenance disaster-resilience infrastructure-maintenance-management vectorized-operations infrastructure-resilience n-step-learning bridge-fleet
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Dec 8, 2025 - Python
Multi-Equipment CBM system using QR-DQN with advanced probability distribution analysis. Coordinated maintenance decision-making for 4 industrial equipment units with realistic anomaly rates (1.9-2.2%), comprehensive risk analysis (VaR/CVaR), and 51-quantile distribution visualization.
reinforcement-learning risk-analysis uncertainty-estimation value-at-risk deep-q-learning predictive-maintenance qr-dqn distributional-rl condition-based-maintenance multi-agent-rl cvar prognostics-health-management disaster-resilience equipment-maintenance infrastructure-prognostics
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Dec 21, 2025 - Python
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