Reinforcement Learning broadly involves Value-based methods and Policy-based Methods. Actor-based methods are suitable for discrete action spaces, whereas Policy-based methods are suitable for continuous action spaces.

A simple Policy Gradient algorithm (a policy-based method) predicts the probability of Actions of an Agent. Since, the Policy is updated using Monte Carlo approach (i.e. taking random samples), the variance of log probabilities produced by the PG algorithm is very high, causing unstable learning. This variance can be reduced by using a Baseline to remove outliers. A good choice to be used as a baseline is the Q-Value of the State, Action pair.

In this session, we will talk about Actor Critic algorithms, which uses two networks - an Actor and a Critic. The actor outputs the Policy, while the Critic gives out the Q-value which can be used to evaluate the Policy. In this zero-sum game, an equilibrium is reached when an ideal policy is discovered.

We will demonstrate with a Code demo, how this algorithm can be used to predict Actions in a Stock market scenario.

In Deep Reinforcement Learning, we know that policy Gradient methods work efficiently in predicting the probability of the Actions, in case of discrete Action Spaces and the exact action itself incase of continous Actions. The Predicted Action is evaluated by another Neural Network. We have used this to train a Robotic Arm to perform specific Tasks.

But what if we have more One Agent in an environment to be trained?.You can have an autonomous group of agents sharing a common environment. These Agents can work Collaboratively towards maximizing their total goal, or they could be interacting Competitively against each other to get the Reward, or they can be interacting in a mixed mode.

In this session, I will talk about Multi Agent Reinforcement Algorithms (MARL) which can be used in either collaborative mode, or competitive mode or a mixed mode between Agents. I will present a brief Code walk through of using the above algorithm in Open AI's Multi Agent Particle Environment.

In this session, data scientists from CellStrat AI Lab will present demos and presentations on cutting-edge AI solutions in :-

• Computer Vision - Image Segmentation with FCN/UNets/DeepLab/ESPNet, Image Processing, Pose Estimation with DensePose
• Natural Language Processing (NLP) - Latest NLP and Text Analytics with BERT, NER, Neural Language Translation etc to solve problems such as text summarization, QnA systems, video captioning etc.
• Reinforcement Learning (RL) - Train Atari Video Games with RL, Augmented Random Search, Deep Q Learning etc. Apply RL techniques for gaming, financial portfolios, driverless cars etc. Train Robots with MuJoCo simulator.
• Driverless Cars - Demo on multi-class roads datasets, path planning and navigation control for cars etc.
• Neural Network Architectures - Faster and Smaller Neural Networks with MorphNet