TD Learning

CellStrat > Research/Blog > TD Learning

Apr

This post discusses temporal difference (TD) methods, used in Reinforcement Learning. It contrasts TD methods with Monte Carlo (MC) methods and dynamic programming. You need to have a thorough understanding of Markov Decision Process (MDP) to understand this post. Prediction and Control : In general, RL methods have two components 1) Prediction / Evaluation : where […]

Posted in: Reinforcement Learning, Robotics,

Tags: epsilon greedy technique, exploration vs exploitation, On-policy vs off-policy, policy improvement, policy iteration, Q learning, reinforcement learning, SARSA, SARSAMax, TD control, TD Learning, Temporal Difference, value iteration,

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,

Jan

Meeting Minutes from CellStrat AI Lab session on Saturday 28th Dec 2019 at Bengaluru

#CellStratAILab #disrupt4.0 #WeCreateAISuperstars The last meetup of Year 2019 at the CellStrat AI Lab saw incredible presentations by our AI Lab Members. First Sujith Kamath presented a superb session on Speech Recognition accompanied by a demo. Speech to Text Recognition is the buzz of the day. Though this area started 7 decades back, it has […]

Posted in: Reinforcement Learning, Speech Applications,

Tags: artificial intelligence, deep learning, machine learning, model free learning, monte carlo, reinforcement learning, Speech applications, speech recognition, Speech to Text, TD 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,