Index of /nptel/computing/Reinforcement Learning

	Name	Last modified	Size	Description
	Parent Directory		-
	1-Tutorial 1 - Probability Basics 1.mp4	2020-07-31 15:46	39M
	10- UCB 1.mp4	2020-07-31 15:46	353M
	11- Concentration Bounds.mp4	2020-07-31 15:48	651M
	12- UCB 1 Theorem.mp4	2020-07-31 15:49	627M
	13- PAC Bounds.mp4	2020-07-31 15:50	808M
	14- Median Elimination.mp4	2020-07-31 15:51	477M
	15- Thompson Sampling.mp4	2020-07-31 15:51	381M
	16- Policy Search.mp4	2020-07-31 15:52	651M
	17- REINFORCE.mp4	2020-07-31 15:53	639M
	18- Contextual Bandits.mp4	2020-07-31 15:54	337M
	19- Full RL Introduction.mp4	2020-07-31 15:55	958M
	2- Tutorial 1-Probability basics2.mp4	2020-07-31 15:56	51M
	20- Returns, Value Functions and MDPs.mp4	2020-07-31 15:57	682M
	21- MDP Modelling.mp4	2020-07-31 15:58	847M
	22- Bellman Equation.mp4	2020-07-31 15:59	373M
	23- Bellman Optimality Equation.mp4	2020-07-31 16:00	757M
	24- Cauchy Sequence and Green.mp4	2020-07-31 16:01	838M
	25- Banach Fixed Point Theorem.mp4	2020-07-31 16:02	696M
	26- Convergence Proof.mp4	2020-07-31 16:03	479M
	27- Lpi Convergence.mp4	2020-07-31 16:04	351M
	28- Value Iteration.mp4	2020-08-01 10:15	640M
	29- Policy Iteration.mp4	2020-08-01 10:15	362M
	3- Tutorial 2-Linear algebra-1.mp4	2020-08-01 10:15	45M
	30- Dynamic Programming.mp4	2020-08-01 10:17	901M
	31- Monte Carlo.mp4	2020-08-01 10:18	602M
	32- Control in Monte Carlo.mp4	2020-08-01 10:19	729M
	33- Off Policy MC.mp4	2020-08-01 10:20	439M
	34- UCT.mp4	2020-08-01 10:21	971M
	35- TD(0).mp4	2020-08-01 10:23	921M
	36- TD(0) Control.mp4	2020-08-01 10:23	572M
	37- Q-Learning.mp4	2020-08-01 10:25	794M
	38- Afterstate.mp4	2020-08-01 10:25	192M
	39- Eligibility Traces.mp4	2020-08-01 10:26	700M
	4- Tutorial 2-Linear algebra-2.mp4	2020-08-01 10:26	42M
	40- Backward View of Eligibility Traces.mp4	2020-08-01 10:28	880M
	41- Eligibility Trace Control.mp4	2020-08-01 10:29	865M
	42- Thompson Sampling Recap.mp4	2020-08-01 10:30	587M
	43- Function Approximation.mp4	2020-08-01 10:31	377M
	44- Linear Parameterization.mp4	2020-08-01 10:31	169M
	45- State Aggregation Methods.mp4	2020-08-01 10:31	251M
	46- Function Approximation and Eligibility Traces.mp4	2020-08-01 10:32	730M
	47- LSTD and LSTDQ.mp4	2020-08-01 10:34	740M
	48- LSPI and Fitted Q.mp4	2020-08-01 10:34	464M
	49- DQN and Fitted Q-Iteration.mp4	2020-08-01 10:34	95M
	5- Introduction to RL.mp4	2020-08-01 10:36	718M
	50- Policy Gradient Approach.mp4	2020-08-01 10:36	120M
	51- Actor Critic and REINFORCE.mp4	2020-08-01 10:36	54M
	52- REINFORCE (cont.mp4	2020-08-01 10:36	115M
	53- Policy Gradient with Function Approximation.mp4	2020-08-01 10:36	94M
	54- Hierarchical Reinforcement Learning.mp4	2020-08-01 10:37	817M
	55- Types of Optimality.mp4	2020-08-01 10:38	590M
	56- Semi Markov Decision Processes.mp4	2020-08-01 10:39	690M
	57- Options.mp4	2020-08-01 10:40	601M
	58- Learning with Options.mp4	2020-08-01 10:41	719M
	59- Hierarchical Abstract Machines.mp4	2020-08-01 10:43	952M
	6- RL Framework and applications.mp4	2020-08-01 10:44	773M
	60- MAXQ.mp4	2020-08-01 10:45	782M
	61- MAXQ Value Function Decomposition.mp4	2020-08-01 10:46	640M
	62- Option Discovery.mp4	2020-08-01 10:47	409M
	63- POMDP Introduction.mp4	2020-08-01 10:48	874M
	64- Solving POMDP.mp4	2020-08-01 10:49	836M
	7- Introduction to Immediate RL.mp4	2020-08-01 10:50	653M
	8- Bandit Optimalities.mp4	2020-08-01 10:51	471M
	9- Value function based methods.mp4	2020-08-01 10:52	607M