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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
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Distributional Reinforcement Learning on Path-dependent Options
CTRLS: Chain-of-Thought Reasoning via Latent State-Transition
ADDQ: Adaptive Distributional Double Q-Learning
A Point-Based Algorithm for Distributional Reinforcement Learning in Partially Observable Domains
Flow Models for Unbounded and Geometry-Aware Distributional Reinforcement Learning
Deep Distributional Learning with Non-crossing Quantile Network
Offline and Distributional Reinforcement Learning for Wireless Communications
RIZE: Regularized Imitation Learning via Distributional Reinforcement Learning
Adaptive Nesterov Accelerated Distributional Deep Hedging for Efficient Volatility Risk Management
A Finite Sample Analysis of Distributional TD Learning with Linear Function Approximation
Robust Probabilistic Model Checking with Continuous Reward Domains
Tackling Uncertainties in Multi-Agent Reinforcement Learning through Integration of Agent Termination Dynamics
Risk-averse policies for natural gas futures trading using distributional reinforcement learning
Beyond CVaR: Leveraging Static Spectral Risk Measures for Enhanced Decision-Making in Distributional Reinforcement Learning
Hedging and Pricing Structured Products Featuring Multiple Underlying Assets
Action Gaps and Advantages in Continuous-Time Distributional Reinforcement Learning
Bellman Diffusion: Generative Modeling as Learning a Linear Operator in the Distribution Space
Offline and Distributional Reinforcement Learning for Radio Resource Management
Foundations of Multivariate Distributional Reinforcement Learning
EX-DRL: Hedging Against Heavy Losses with EXtreme Distributional Reinforcement Learning
Bellman Unbiasedness: Toward Provably Efficient Distributional Reinforcement Learning with General Value Function Approximation
On Policy Evaluation Algorithms in Distributional Reinforcement Learning
PG-Rainbow: Using Distributional Reinforcement Learning in Policy Gradient Methods
Policy Gradient Methods for Risk-Sensitive Distributional Reinforcement Learning with Provable Convergence
CTD4 -- A Deep Continuous Distributional Actor-Critic Agent with a Kalman Fusion of Multiple Critics
Statistical Efficiency of Distributional Temporal Difference Learning and Freedman's Inequality in Hilbert Spaces
Provable Risk-Sensitive Distributional Reinforcement Learning with General Function Approximation
Uncertainty-Aware Transient Stability-Constrained Preventive Redispatch: A Distributional Reinforcement Learning Approach
A Distributional Analogue to the Successor Representation
Near-Minimax-Optimal Distributional Reinforcement Learning with a Generative Model
Echoes of Socratic Doubt: Embracing Uncertainty in Calibrated Evidential Reinforcement Learning
More Benefits of Being Distributional: Second-Order Bounds for Reinforcement Learning
Distributional Off-policy Evaluation with Bellman Residual Minimization
A Robust Quantile Huber Loss With Interpretable Parameter Adjustment In Distributional Reinforcement Learning
Distributional Reinforcement Learning-based Energy Arbitrage Strategies in Imbalance Settlement Mechanism
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