Markov Reward Process

CellStrat > Research/Blog > Markov Reward Process
13
Apr
A Summary of Model-Free RL Algorithms

#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,
19
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,