markov decision process

CellStrat > Research/Blog > markov decision process

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,

Aug

Meeting Minutes from AI Lab Hands-On Workshop on Saturday 24th Aug in Bengaluru

#CellStratAILab #disrupt4.0 #WeCreateAISuperstars Last Saturday, our team lead for Reinforcement Learning (RL) Shubha Manikarnike presented a fabulous hands-on workshop on RL and it’s various algorithms such as Markov Decision Process (MDP), Policy Gradients, Bellman equation, Q-learning etc. The session started with an Introduction to RL. There was a comparison on how this is different from […]

Posted in: Deep Learning, Reinforcement Learning,

Tags: Bellman equation, epsilon greedy technique, exploration vs exploitation, Frozen Lake, markov decision process, openAI Gym, Q learning, reinforcement learning, rewards based learning,

May

Temporal Difference Learning

Reinforcement Learning (RL) is set to be the next big thing in the world of AI and Machine Learning. RL models learn by accumulating rewards for designated actions in certain states. Essentially it is a (State, Action, Reward) optimization system. TD Learning is one type of RL algo which helps solve the problem when state […]

Posted in: Reinforcement Learning,

Tags: artificial intelligence, deep learning, machine learning, markov decision process, mdp, reinforcement learning, TD Learning, Temporal Difference Learning,