Distributional Reinforcement Learning
31 papers with code • 0 benchmarks • 0 datasets
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
Benchmarks
These leaderboards are used to track progress in Distributional Reinforcement Learning
Latest papers
A Distributional Analogue to the Successor Representation
jessefarebro/distributional-sr
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This paper contributes a new approach for distributional reinforcement learning which elucidates a clean separation of transition structure and reward in the learning process.
A Robust Quantile Huber Loss With Interpretable Parameter Adjustment In Distributional Reinforcement Learning
pmalekzadeh/A-robust-quantile-huber-loss
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Distributional Reinforcement Learning (RL) estimates return distribution mainly by learning quantile values via minimizing the quantile Huber loss function, entailing a threshold parameter often selected heuristically or via hyperparameter search, which may not generalize well and can be suboptimal.
Distributional Bellman Operators over Mean Embeddings
google-deepmind/sketch_dqn
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We propose a novel algorithmic framework for distributional reinforcement learning, based on learning finite-dimensional mean embeddings of return distributions.
Estimation and Inference in Distributional Reinforcement Learning
zhangliangyu32/estimationandinferencedistributionalrl
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This implies the distributional policy evaluation problem can be solved with sample efficiency.
Variance Control for Distributional Reinforcement Learning
kuangqi927/qem
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Although distributional reinforcement learning (DRL) has been widely examined in the past few years, very few studies investigate the validity of the obtained Q-function estimator in the distributional setting.
Distributional Model Equivalence for Risk-Sensitive Reinforcement Learning
tylerkastner/distribution-equivalence
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NeurIPS 2023
We consider the problem of learning models for risk-sensitive reinforcement learning.
Distributional constrained reinforcement learning for supply chain optimization
jaimesabalimperial/jaisalab
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We introduce Distributional Constrained Policy Optimization (DCPO), a novel approach for reliable constraint satisfaction in RL.
Trust Region-Based Safe Distributional Reinforcement Learning for Multiple Constraints
rllab-snu/safe-distributional-actor-critic
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NeurIPS 2023
In safety-critical robotic tasks, potential failures must be reduced, and multiple constraints must be met, such as avoiding collisions, limiting energy consumption, and maintaining balance.
Risk-Sensitive Policy with Distributional Reinforcement Learning
thibauttheate/risk-sensitive-policy-with-distributional-reinforcement-learning
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Classical reinforcement learning (RL) techniques are generally concerned with the design of decision-making policies driven by the maximisation of the expected outcome.
Intelligent Resource Allocation in Joint Radar-Communication With Graph Neural Networks
joleeson/Directional-JRC
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IEEE Transactions on Vehicular Technology 2022
In this paper, we propose a framework for intelligent vehicles to conduct JRC, with minimal prior knowledge of the system model and a tunable performance balance, in an environment where surrounding vehicles execute radar detection periodically, which is typical in contemporary protocols.
