… We present the first approach towards learning a risk-averse RL policy for high-… riskaverse
algorithm. O-RAAC is compatible with many common risk-averse criteria such as coherentrisk …

In this work, we address risk-averse Bayes-adaptive reinforcement learning. We pose the
problem of optimising the conditional value at risk (CVaR) of the total return in Bayes-adaptive …

… of any risk-averse policy gradient (PG) method. We demonstrate improved risk aversion in
… , including in scenarios where standard risk-averse PG completely fails. Our results and …

… Reward Design and Risk Modeling To train risk averse agents, we propose an asymmetric
reward function design such that good behavior receives small positive rewards and risky …

… We conclude (see Table 1) that most of the subjects are risk averse for positive and risk
seeking for negative TD errors. The result is consistent with previous studies from the economics …

… risk-averse reinforcement learning can be used to hedge options. We apply a state-of-the-art
risk-averse … of agents each characterized by a different risk aversion, so to be able to span …

… Although this choice strategy was not selected for, we rigorously prove that risk aversion …
optimal reinforcement learning can directly explain complex behaviors such as risk aversion …

… as risk aversion increases. This paper is organized as follows: in Section 2 we introduce the
Reinforcement Learning … of the risk measures adopted and Multi-Objective Reinforcement …

… We provide a new theory of risk-sensitive control, 2. formulate reinforcement learning
algorithms within this framework which require only minor changes of already known and widely …

… In summary, we have presented a novel model of evolved reinforcement learning agents, …
optimal reinforcement learning can directly explain complex behaviors such as risk aversion…