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
Most implemented papers
Unifying Cardiovascular Modelling with Deep Reinforcement Learning for Uncertainty Aware Control of Sepsis Treatment
Sepsis is a potentially life threatening inflammatory response to infection or severe tissue damage.
Distributional Reinforcement Learning with Unconstrained Monotonic Neural Networks
ThibautTheate/Unconstrained-Monotonic-Deep-Q-Network-algorithm
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The results highlight the main strengths and weaknesses associated with each probability metric together with an important limitation of the Wasserstein distance.
Conservative Offline Distributional Reinforcement Learning
JasonMa2016/CODAC
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NeurIPS 2021
We prove that CODAC learns a conservative return distribution -- in particular, for finite MDPs, CODAC converges to an uniform lower bound on the quantiles of the return distribution; our proof relies on a novel analysis of the distributional Bellman operator.
Exploring the Training Robustness of Distributional Reinforcement Learning against Noisy State Observations
In real scenarios, state observations that an agent observes may contain measurement errors or adversarial noises, misleading the agent to take suboptimal actions or even collapse while training.
A Cramér Distance perspective on Quantile Regression based Distributional Reinforcement Learning
alherit/cr-dqn
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NeurIPS 2021
Distributional reinforcement learning (DRL) extends the value-based approach by approximating the full distribution over future returns instead of the mean only, providing a richer signal that leads to improved performances.
Distributional Reinforcement Learning for Multi-Dimensional Reward Functions
zpschang/MD3QN
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NeurIPS 2021
To fully inherit the benefits of distributional RL and hybrid reward architectures, we introduce Multi-Dimensional Distributional DQN (MD3QN), which extends distributional RL to model the joint return distribution from multiple reward sources.
Two steps to risk sensitivity
Distributional reinforcement learning (RL) -- in which agents learn about all the possible long-term consequences of their actions, and not just the expected value -- is of great recent interest.
Conjugated Discrete Distributions for Distributional Reinforcement Learning
bjliaa/c2d
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In this work we continue to build upon recent advances in reinforcement learning for finite Markov processes.
Adaptive Risk-Tendency: Nano Drone Navigation in Cluttered Environments with Distributional Reinforcement Learning
Enabling the capability of assessing risk and making risk-aware decisions is essential to applying reinforcement learning to safety-critical robots like drones.
Gamma and Vega Hedging Using Deep Distributional Reinforcement Learning
rotmanfinhub/gamma-vega-rl-hedging
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We show how D4PG can be used in conjunction with quantile regression to develop a hedging strategy for a trader responsible for derivatives that arrive stochastically and depend on a single underlying asset.
