DevanshKandpal: Bot: wrap bare course codes in wikilinks

2026-04-14T16:26:36Z

Bot: wrap bare course codes in wikilinks

← Older revision		Revision as of 16:26, 14 April 2026
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	\| credits = 4		\| credits = 4
	\| credit_structure = 3-0-2		\| credit_structure = 3-0-2
	\| pre_requisites = Any one of COL333 / COL774 / ELL784 /		\| pre_requisites = Any one of [[COL333]] / [[COL774]] / [[ELL784]] /
	\| overlaps = AIL722, ELL729		\| overlaps = [[AIL722]], [[ELL729]]
	}}		}}

	== COL777 : Deep Reinforcement Learning ==		== COL777 : Deep Reinforcement Learning ==
	COL333/COL671 Introduction and Basics of RL, Markov Decision Processes (MDPs), Dynamic Programming, Monte Carlo Methods (Prediction), Temporal difference Methods (Prediction), Monte Carlo, TD Method (Control), N-step TD, Eligibility Traces, Model based RL, (Action-)Value Function Approximation, Value Function Approximation, Policy Gradient, Policy Gradient, Recent Applications, Misc./Advanced Topics. COL778 principles of Autonomous Systems Credits: 4 (3-0-2) ELL409 Intelligent Agent/Robot Representation. Software and simulation tools. Classical Planning. Anytime and incremental search. Decision-making under Uncertainty. Reinforcement Learning. Imitation learning. State Estimation using Bayesian Networks. Particle Filtering. World Representations. Map representations. Exploration and coverage. Interaction and Intent Inference. Execution and monitoring. Advanced epics and case study. Programming Assignments. Courses of Study 2024-2025 Computer Science and Engineering 166		[[COL333]]/[[COL671]] Introduction and Basics of RL, Markov Decision Processes (MDPs), Dynamic Programming, Monte Carlo Methods (Prediction), Temporal difference Methods (Prediction), Monte Carlo, TD Method (Control), N-step TD, Eligibility Traces, Model based RL, (Action-)Value Function Approximation, Value Function Approximation, Policy Gradient, Policy Gradient, Recent Applications, Misc./Advanced Topics. [[COL778]] principles of Autonomous Systems Credits: 4 (3-0-2) [[ELL409]] Intelligent Agent/Robot Representation. Software and simulation tools. Classical Planning. Anytime and incremental search. Decision-making under Uncertainty. Reinforcement Learning. Imitation learning. State Estimation using Bayesian Networks. Particle Filtering. World Representations. Map representations. Exploration and coverage. Interaction and Intent Inference. Execution and monitoring. Advanced epics and case study. Programming Assignments. Courses of Study 2024-2025 Computer Science and Engineering 166

Prashantt492: Creating course page via bot

2026-03-04T10:00:25Z

Creating course page via bot

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{{Infobox Course
| code = COL777
| name = Deep Reinforcement Learning
| credits = 4
| credit_structure = 3-0-2
| pre_requisites = Any one of COL333 / COL774 / ELL784 /
| overlaps = AIL722, ELL729
}}

== COL777 : Deep Reinforcement Learning ==
COL333/COL671 Introduction and Basics of RL, Markov Decision Processes (MDPs), Dynamic Programming, Monte Carlo Methods (Prediction), Temporal difference Methods (Prediction), Monte Carlo, TD Method (Control), N-step TD, Eligibility Traces, Model based RL, (Action-)Value Function Approximation, Value Function Approximation, Policy Gradient, Policy Gradient, Recent Applications, Misc./Advanced Topics. COL778 principles of Autonomous Systems Credits: 4 (3-0-2) ELL409 Intelligent Agent/Robot Representation. Software and simulation tools. Classical Planning. Anytime and incremental search. Decision-making under Uncertainty. Reinforcement Learning. Imitation learning. State Estimation using Bayesian Networks. Particle Filtering. World Representations. Map representations. Exploration and coverage. Interaction and Intent Inference. Execution and monitoring. Advanced epics and case study. Programming Assignments. Courses of Study 2024-2025 Computer Science and Engineering 166

COL777 - Revision history

DevanshKandpal: Bot: wrap bare course codes in wikilinks

Prashantt492: Creating course page via bot