**Decision making**

**AI BASED RISK MAP**

**how to read graph**

**case study**

**solving risk problems**

**fuRTher reading**

**DECISION MAKING**

**AI based risk heat map**is a tool used to present the results of a invest-risk assessment process visually and in a meaningful and concise way.

**HOW TO READ GRAPH cASE STUDY**

**STRONG BUY**

**n parameter**(The time series to forecast),

**x axis:Likelihood %;**

y axis:Potential Impact %

y axis:Potential Impact %

**SOLVING RISK PROBLEMS**

**FURTHER READING**

**Deep Reinforcement Learning in Large Discrete Action Spaces**

Applying reasoning in an environment with a large number of discrete actions to bring reinforcement learning to a wider class of problems.

**Deep Reinforcement Learning with Attention for Slate Markov Decision Processes with High-Dimensional States and Actions**

Introducing slate Markov Decision Processes (MDPs), a formulation that allows reinforcement learning to be applied to recommender system problems.

**Massively Parallel Methods for Deep Reinforcement Learning**

Presenting the first massively distributed architecture for deep reinforcement learning.

**Adaptive Lambda Least-Squares Temporal Difference Learning**

Learning to select the best value of Î» (which controls the timescale of updates) for TD(Î») to ensure the best result when trading off bias against variance.

**Learning from Demonstrations for Real World Reinforcement Learning**

Presenting Deep Q-learning from Demonstrations (DQfD), an algorithm that leverages data from previous control of a system to accelerate learning.

**Value-Decomposition Networks For Cooperative Multi-Agent Learning**

Studying the problem of cooperative multi-agent reinforcement learning with a single joint reward signal.

**Many Paths to Equilibrium: GANs Do Not Need to Decrease a Divergence At Every Step**

Demonstrating an alternative view of the training of GANs.

**Risk-Constrained Reinforcement Learning with Percentile Risk Criteria**

Presenting efficient reinforcement learning algorithms for risk-constrained Markov decision processes (MDPs) and demonstrating their effectiveness in an optimal stopping problem and an online marketing application.