Value-based methods

CellStrat > Research/Blog > Value-based methods

Apr

#CellStratAILab #disrupt4.0 #WeCreateAISuperstars #AlwaysUpskilling Reinforcement Learning (RL) refers to training agents with help of incentive-driven environments. RL typically involves a tuple of <state, action, reward> paradigm, which means that the agent has action choices to make in various states, and each action entails a potential reward. This also means that each state has a “value” […]

Posted in: Reinforcement Learning, Robotics,

Tags: Actor Critic, Actor Critic method, AI lab, cartpole, DDPG, deep Q learning, deep Q network, Deep Reinforcement Learning, deterministic policy, DQN, DRL course, gaming, markov decision process, markov process, Markov Reward Process, mdp, model-based RL, model-free RL, monte carlo, off-policy, on-policy, policy based methods, policy gradients, PPO, Q learning, Q table, Rainbow method, Reinforcement learning course, reward function, RL course, RL for gaming, RL training, SAC, stochastic policy, TD Learning, TD3, training in reinforcement learning, TRPO, Value-based methods,

Mar

RL with Actor-Critic Methods

#CellStratAILab #disrupt4.0 #WeCreateAISuperstars #AlwaysUpskilling Minutes from Saturday 14th March 2020 AI Lab Workshop at BLR :- Session Presenter : SHUBHA M., Deep Reinforcement Learning Researcher, CellStrat AI Lab Last Saturday, our Reinforcement Learning Team Lead Shubha M. presented a fantastic presentation and workshop on Actor-Critic method used in RL. She also demonstrated a demo of this technique for Stock Market predictions. […]

Posted in: Reinforcement Learning, Robotics,

Tags: Actor Critic, deep Q learning, Deep Reinforcement Learning, DRL, markov decision process, Markov Reward Process, mdp, MRP, policy based methods, policy gradients, Q Actor Critic, Q learning, REINFORCE algorithm, reinforcement learning, Value-based methods,

Mar

Face Recognition with MTCNN and FaceNet; RL with Proximal Policy Optimization

#CellStratAILab #disrupt4.0 #WeCreateAISuperstars #AlwaysUpskilling Minutes from Saturday 7th March 2020 AI Lab meetup at BLR :- Last Saturday, we had excellent sessions in the AI Lab meetup. Face Recognition with MTCNN and FaceNet :- First Amit Kumar presented a detailed overview of Face Recognition with MTCNN and FaceNet. Face Recognition involves a pipeline of Face […]

Posted in: Computer Vision, Deep Learning, Reinforcement Learning, Security,

Tags: Advantage Function, Face Recognition, facenet, Important Sampling, MTCNN, MTCNN face detector, O-Net, PG algo, policy based methods, policy gradients, PPO, proximal policy optimization, R-Net, reinforcement learning, Triplet Loss, Value-based methods, Vanilla Policy Gradient, VPG,