COL777: Difference between revisions
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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 | ||
Latest revision as of 16:26, 14 April 2026
| COL777 | |
|---|---|
| Deep Reinforcement Learning | |
| Credits | 4 |
| 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