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Distributional Reinforcement Learning

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Value distribution is the distribution of the random return received by a reinforcement learning agent. it been used for a specific purpose such as implementing risk-aware behaviour.

We have random return Z whose expectation is the value Q. This random return is also described by a recursive equation, but one of a distributional nature

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/IndustAI/risk-and-uncertainty

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Estimating Risk and Uncertainty in Deep Reinforcement Learning

https://github.com/JasonMa2016/CODAC

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Conservative Offline Distributional Reinforcement Learning

https://github.com/ShangtongZhang/DeepRL

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QUOTA: The Quantile Option Architecture for Reinforcement Learning

https://github.com/ThibautTheate/Unconstrained-Monotonic-Deep-Q-Network-algorithm

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Distributional Reinforcement Learning with Unconstrained Monotonic Neural Networks

https://github.com/UoA-CARES/cares_reinforcement_learning/blob/main/cares_reinforcement_learning/algorithm/policy/CTD4.py

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CTD4 -- A Deep Continuous Distributional Actor-Critic Agent with a Kalman Fusion of Multiple Critics

https://github.com/acstutts/ceqr-dqn

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Echoes of Socratic Doubt: Embracing Uncertainty in Calibrated Evidential Reinforcement Learning

https://github.com/adibka/RIZE

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RIZE: Regularized Imitation Learning via Distributional Reinforcement Learning

https://github.com/alex-yanranwang/consdred-smpc

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Constrained Reinforcement Learning using Distributional Representation for Trustworthy Quadrotor UAV Tracking Control

https://github.com/alherit/cr-dqn

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A Cramér Distance perspective on Quantile Regression based Distributional Reinforcement Learning

https://github.com/bjliaa/c2d

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Conjugated Discrete Distributions for Distributional Reinforcement Learning

https://github.com/bommehd/addq

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ADDQ: Adaptive Distributional Double Q-Learning

https://github.com/boschresearch/dist-mbrl

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Value-Distributional Model-Based Reinforcement Learning

https://github.com/crgagne/twosteps_neurips2021

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Two steps to risk sensitivity

https://github.com/datake/robustdistrl

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Exploring the Training Robustness of Distributional Reinforcement Learning against Noisy State Observations

https://github.com/datake/sinkhorndistrl

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Distributional Reinforcement Learning with Regularized Wasserstein Loss

https://github.com/google-deepmind/sketch_dqn

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Distributional Bellman Operators over Mean Embeddings

https://github.com/hse1223/distributional-ope-ebrm

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Distributional Off-policy Evaluation with Bellman Residual Minimization

https://github.com/jaimesabalimperial/jaisalab

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Distributional constrained reinforcement learning for supply chain optimization

https://github.com/jessefarebro/distributional-sr

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A Distributional Analogue to the Successor Representation

https://github.com/kevinzhou497/distcb

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The Benefits of Being Distributional: Small-Loss Bounds for Reinforcement Learning

https://github.com/kuangqi927/qem

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Variance Control for Distributional Reinforcement Learning

https://github.com/mehrdadmoghimi/qrsrm

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Beyond CVaR: Leveraging Static Spectral Risk Measures for Enhanced Decision-Making in Distributional Reinforcement Learning

https://github.com/nikonikolov/rltf

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Information-Directed Exploration for Deep Reinforcement Learning

https://github.com/parshakova/GAMS-for-Data-Efficient-Learning

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Distributional Reinforcement Learning for Energy-Based Sequential Models

https://github.com/pmalekzadeh/A-robust-quantile-huber-loss

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A Robust Quantile Huber Loss With Interpretable Parameter Adjustment In Distributional Reinforcement Learning

https://github.com/pmalekzadeh/ex-drl

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EX-DRL: Hedging Against Heavy Losses with EXtreme Distributional Reinforcement Learning

https://github.com/rllab-snu/safe-distributional-actor-critic

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Trust Region-Based Safe Distributional Reinforcement Learning for Multiple Constraints

https://github.com/robinzixuan/IQN_Agent

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IGN : Implicit Generative Networks

https://github.com/rotmanfinhub/gamma-vega-rl-hedging

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Gamma and Vega Hedging Using Deep Distributional Reinforcement Learning

https://github.com/somnathhazra/uncertainties_marl

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Tackling Uncertainties in Multi-Agent Reinforcement Learning through Integration of Agent Termination Dynamics

https://github.com/thanhnguyentang/mmdrl

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Distributional Reinforcement Learning via Moment Matching

https://github.com/thibauttheate/risk-sensitive-policy-with-distributional-reinforcement-learning

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Risk-Sensitive Policy with Distributional Reinforcement Learning

https://github.com/thxsxth/POMDP_RLSepsis

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Unifying Cardiovascular Modelling with Deep Reinforcement Learning for Uncertainty Aware Control of Sepsis Treatment

https://github.com/tudelft/risk-sensitive-rl

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Adaptive Risk-Tendency: Nano Drone Navigation in Cluttered Environments with Distributional Reinforcement Learning

https://github.com/tylerkastner/distribution-equivalence

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Distributional Model Equivalence for Risk-Sensitive Reinforcement Learning

https://github.com/zhangliangyu32/estimationandinferencedistributionalrl

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Estimation and Inference in Distributional Reinforcement Learning

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