Other
Reinforcement Learning Specialization
Torrent info
Torrent Files List
[TutsNode.net] - Reinforcement Learning Specialization (Size: 4.61 GB) (Files: 699)
[TutsNode.net] - Reinforcement Learning Specialization
fundamentals-of-reinforcement-learning
05_dynamic-programming
03_generalized-policy-iteration
04_warren-powell-approximate-dynamic-programming-for-fleet-management-long.mp4
04_warren-powell-approximate-dynamic-programming-for-fleet-management-long.en.srt
04_warren-powell-approximate-dynamic-programming-for-fleet-management-long.en.txt
03_warren-powell-approximate-dynamic-programming-for-fleet-management-short.en.srt
02_efficiency-of-dynamic-programming.en.srt
03_warren-powell-approximate-dynamic-programming-for-fleet-management-short.en.txt
01_flexibility-of-the-policy-iteration-framework.en.srt
02_efficiency-of-dynamic-programming.en.txt
05_week-4-summary.en.txt
06_chapter-summary_instructions.html
05_week-4-summary.en.srt
01_flexibility-of-the-policy-iteration-framework.en.txt
06_chapter-summary_RLbook2018.pdf
03_warren-powell-approximate-dynamic-programming-for-fleet-management-short.mp4
02_efficiency-of-dynamic-programming.mp4
01_flexibility-of-the-policy-iteration-framework.mp4
05_week-4-summary.mp4
04_weekly-assessment
01_dynamic-programming_quiz.html
02_optimal-policies-with-dynamic-programming_instructions.html
01_policy-evaluation-prediction
04_iterative-policy-evaluation.en.srt
04_iterative-policy-evaluation.en.txt
03_policy-evaluation-vs-control.en.srt
01_module-4-learning-objectives_instructions.html
02_weekly-reading_instructions.html
03_policy-evaluation-vs-control.en.txt
02_weekly-reading_RLbook2018.pdf
04_iterative-policy-evaluation.mp4
03_policy-evaluation-vs-control.mp4
02_policy-iteration-control
02_policy-iteration.en.srt
02_policy-iteration.en.txt
01_policy-improvement.en.srt
01_policy-improvement.en.txt
02_policy-iteration.mp4
01_policy-improvement.mp4
05_course-wrap-up
01_congratulations.en.srt
01_congratulations.en.txt
01_congratulations.mp4
02_an-introduction-to-sequential-decision-making
04_weekly-assessment
01_sequential-decision-making_quiz.html
02_bandits-and-exploration-exploitation_instructions.html
03_exploration-vs-exploitation-tradeoff
04_jonathan-langford-contextual-bandits-for-real-world-reinforcement-lea
01_what-is-the-trade-off.en.srt
02_optimistic-initial-values.en.srt
03_upper-confidence-bound-ucb-action-selection.en.srt
05_week-1-summary.en.txt
06_chapter-summary_instructions.html
01_what-is-the-trade-off.en.txt
02_optimistic-initial-values.en.txt
05_week-1-summary.en.srt
03_upper-confidence-bound-ucb-action-selection.en.txt
06_chapter-summary_RLbook2018.pdf
01_what-is-the-trade-off.mp4
02_optimistic-initial-values.mp4
03_upper-confidence-bound-ucb-action-selection.mp4
05_week-1-summary.mp4
01_the-k-armed-bandit-problem
02_weekly-reading_RLbook2018.pdf
03_sequential-decision-making-with-evaluative-feedback.en.srt
01_module-1-learning-objectives_instructions.html
02_weekly-reading_instructions.html
03_sequential-decision-making-with-evaluative-feedback.en.txt
03_sequential-decision-making-with-evaluative-feedback.mp4
02_what-to-learn-estimating-action-values
02_estimating-action-values-incrementally.en.srt
01_learning-action-values.en.srt
02_estimating-action-values-incrementally.en.txt
01_learning-action-values.en.txt
02_estimating-action-values-incrementally.mp4
01_learning-action-values.mp4
01_welcome-to-the-course
01_course-introduction
06_read-me-pre-requisites-and-learning-objectives_Fundamentals_of_Reinforcement_Learning__Learning_Objectives.pdf
02_course-introduction.en.txt
05_reinforcement-learning-textbook_RLbook2018.pdf
03_meet-your-instructors.en.srt
02_course-introduction.en.srt
03_meet-your-instructors.en.txt
01_specialization-introduction.en.txt
05_reinforcement-learning-textbook_instructions.html
06_read-me-pre-requisites-and-learning-objectives_instructions.html
04_your-specialization-roadmap.en.srt
01_specialization-introduction.en.srt
04_your-specialization-roadmap.en.txt
03_meet-your-instructors.mp4
02_course-introduction.mp4
01_specialization-introduction.mp4
04_your-specialization-roadmap.mp4
04_value-functions-bellman-equations
04_weekly-assessment
02_graded-value-functions-and-bellman-equations_exam.html
01_practice-value-functions-and-bellman-equations_quiz.html
03_optimality-optimal-policies-value-functions
01_optimal-policies.en.srt
03_using-optimal-value-functions-to-get-optimal-policies.en.srt
02_optimal-value-functions.en.srt
03_using-optimal-value-functions-to-get-optimal-policies.en.txt
01_optimal-policies.en.txt
04_week-3-summary.en.srt
05_chapter-summary_RLbook2018.pdf
05_chapter-summary_instructions.html
02_optimal-value-functions.en.txt
04_week-3-summary.en.txt
01_optimal-policies.mp4
03_using-optimal-value-functions-to-get-optimal-policies.mp4
04_week-3-summary.mp4
02_optimal-value-functions.mp4
01_policies-and-value-functions
05_rich-sutton-and-andy-barto-a-brief-history-of-rl.en.srt
04_value-functions.en.srt
02_weekly-reading_instructions.html
03_specifying-policies.en.srt
04_value-functions.en.txt
05_rich-sutton-and-andy-barto-a-brief-history-of-rl.en.txt
03_specifying-policies.en.txt
01_module-3-learning-objectives_instructions.html
02_weekly-reading_RLbook2018.pdf
05_rich-sutton-and-andy-barto-a-brief-history-of-rl.mp4
04_value-functions.mp4
03_specifying-policies.mp4
02_bellman-equations
01_bellman-equation-derivation.en.srt
02_why-bellman-equations.en.srt
01_bellman-equation-derivation.en.txt
02_why-bellman-equations.en.txt
01_bellman-equation-derivation.mp4
02_why-bellman-equations.mp4
03_markov-decision-processes
02_goal-of-reinforcement-learning
02_michael-littman-the-reward-hypothesis.en.srt
02_michael-littman-the-reward-hypothesis.en.txt
01_the-goal-of-reinforcement-learning.en.txt
01_the-goal-of-reinforcement-learning.en.srt
02_michael-littman-the-reward-hypothesis.mp4
01_the-goal-of-reinforcement-learning.mp4
04_weekly-assesment
01_mdps_quiz.html
02_graded-assignment-describe-three-mdps_peer_assignment_instructions.html
01_introduction-to-markov-decision-processes
03_markov-decision-processes.en.srt
01_module-2-learning-objectives_instructions.html
02_weekly-reading_instructions.html
04_examples-of-mdps.en.srt
03_markov-decision-processes.en.txt
04_examples-of-mdps.en.txt
02_weekly-reading_RLbook2018.pdf
03_markov-decision-processes.mp4
04_examples-of-mdps.mp4
03_continuing-tasks
01_continuing-tasks.en.srt
02_examples-of-episodic-and-continuing-tasks.en.txt
03_week-2-summary.en.srt
03_week-2-summary.en.txt
02_examples-of-episodic-and-continuing-tasks.en.srt
01_continuing-tasks.en.txt
01_continuing-tasks.mp4
02_examples-of-episodic-and-continuing-tasks.mp4
03_week-2-summary.mp4
complete-reinforcement-learning-system
01_welcome-to-the-final-capstone-course
01_course-introduction
01_course-4-introduction.en.txt
03_reinforcement-learning-textbook_instructions.html
04_pre-requisites-and-learning-objectives_A_Complete_Reinforcement_Learning_System_Capstone__Learn
02_meet-your-instructors.en.srt
02_meet-your-instructors.en.txt
01_course-4-introduction.en.srt
04_pre-requisites-and-learning-objectives_instructions.html
03_reinforcement-learning-textbook_RLbook2018.pdf
02_meet-your-instructors.mp4
01_course-4-introduction.mp4
03_milestone-2-choosing-the-right-algorithm
02_project-resources
03_lets-review-average-reward-a-new-way-of-formulating-control-problems.en.srt
01_lets-review-expected-sarsa.en.txt
02_lets-review-what-is-q-learning.en.txt
05_csaba-szepesvari-on-problem-landscape.en.srt
03_lets-review-average-reward-a-new-way-of-formulating-control-problems.en.txt
04_lets-review-actor-critic-algorithm.en.srt
05_csaba-szepesvari-on-problem-landscape.en.txt
06_andy-and-rich-advice-for-students.en.srt
02_lets-review-what-is-q-learning.en.srt
04_lets-review-actor-critic-algorithm.en.txt
01_lets-review-expected-sarsa.en.srt
06_andy-and-rich-advice-for-students.en.txt
05_csaba-szepesvari-on-problem-landscape.mp4
06_andy-and-rich-advice-for-students.mp4
03_lets-review-average-reward-a-new-way-of-formulating-control-problems.mp4
04_lets-review-actor-critic-algorithm.mp4
02_lets-review-what-is-q-learning.mp4
01_lets-review-expected-sarsa.mp4
01_weekly-learning-goals
01_meeting-with-niko-choosing-the-learning-algorithm.en.txt
01_meeting-with-niko-choosing-the-learning-algorithm.en.srt
01_meeting-with-niko-choosing-the-learning-algorithm.mp4
02_milestone-1-formalize-word-problem-as-mdp
02_project-resources
02_lets-review-examples-of-episodic-and-continuing-tasks.en.txt
01_lets-review-markov-decision-processes.en.srt
01_lets-review-markov-decision-processes.en.txt
02_lets-review-examples-of-episodic-and-continuing-tasks.en.srt
01_lets-review-markov-decision-processes.mp4
02_lets-review-examples-of-episodic-and-continuing-tasks.mp4
01_final-project-milestone-1
02_andy-barto-on-what-are-eligibility-traces-and-why-are-they-so-named.en.srt
01_initial-project-meeting-with-martha-formalizing-the-problem.en.srt
02_andy-barto-on-what-are-eligibility-traces-and-why-are-they-so-named.en.txt
01_initial-project-meeting-with-martha-formalizing-the-problem.en.txt
02_andy-barto-on-what-are-eligibility-traces-and-why-are-they-so-named.mp4
01_initial-project-meeting-with-martha-formalizing-the-problem.mp4
06_milestone-5-submit-your-parameter-study
02_project-resources
02_joelle-pineau-about-rl-that-matters.en.srt
02_joelle-pineau-about-rl-that-matters.en.txt
01_lets-review-comparing-td-and-monte-carlo.en.srt
01_lets-review-comparing-td-and-monte-carlo.en.txt
02_joelle-pineau-about-rl-that-matters.mp4
01_lets-review-comparing-td-and-monte-carlo.mp4
03_congratulations
01_meeting-with-martha-discussing-your-results.en.txt
02_course-wrap-up.en.srt
02_course-wrap-up.en.txt
03_specialization-wrap-up.en.srt
01_meeting-with-martha-discussing-your-results.en.srt
03_specialization-wrap-up.en.txt
03_specialization-wrap-up.mp4
01_meeting-with-martha-discussing-your-results.mp4
02_course-wrap-up.mp4
01_weekly-learning-goals
01_meeting-with-adam-parameter-studies-in-rl.en.srt
01_meeting-with-adam-parameter-studies-in-rl.en.txt
01_meeting-with-adam-parameter-studies-in-rl.mp4
04_milestone-3-identify-key-performance-parameters
01_weekly-learning-goals
01_agent-architecture-meeting-with-martha-overview-of-design-choices.en.srt
01_agent-architecture-meeting-with-martha-overview-of-design-choices.en.txt
01_agent-architecture-meeting-with-martha-overview-of-design-choices.mp4
02_project-resources
02_drew-bagnell-on-system-id-optimal-control.en.srt
03_susan-murphy-on-rl-in-mobile-health.en.srt
02_drew-bagnell-on-system-id-optimal-control.en.txt
03_susan-murphy-on-rl-in-mobile-health.en.txt
01_lets-review-non-linear-approximation-with-neural-networks.en.srt
01_lets-review-non-linear-approximation-with-neural-networks.en.txt
02_drew-bagnell-on-system-id-optimal-control.mp4
03_susan-murphy-on-rl-in-mobile-health.mp4
01_lets-review-non-linear-approximation-with-neural-networks.mp4
05_milestone-4-implement-your-agent
02_project-resources
02_lets-review-expected-sarsa-with-function-approximation.en.txt
01_lets-review-optimization-strategies-for-nns.en.srt
05_martin-riedmiller-on-the-collect-and-infer-framework-for-data-efficient-rl.en.srt
04_meeting-with-martha-in-depth-on-experience-replay.en.srt
03_lets-review-dyna-q-learning-in-a-simple-maze.en.srt
05_martin-riedmiller-on-the-collect-and-infer-framework-for-data-efficient-rl.en.txt
04_meeting-with-martha-in-depth-on-experience-replay.en.txt
01_lets-review-optimization-strategies-for-nns.en.txt
03_lets-review-dyna-q-learning-in-a-simple-maze.en.txt
02_lets-review-expected-sarsa-with-function-approximation.en.srt
05_martin-riedmiller-on-the-collect-and-infer-framework-for-data-efficient-rl.mp4
04_meeting-with-martha-in-depth-on-experience-replay.mp4
01_lets-review-optimization-strategies-for-nns.mp4
03_lets-review-dyna-q-learning-in-a-simple-maze.mp4
02_lets-review-expected-sarsa-with-function-approximation.mp4
01_weekly-learning-goals
01_meeting-with-adam-getting-the-agent-details-right.en.srt
01_meeting-with-adam-getting-the-agent-details-right.en.txt
01_meeting-with-adam-getting-the-agent-details-right.mp4
sample-based-learning-methods
01_welcome-to-the-course
01_course-introduction
04_read-me-pre-requisites-and-learning-objectives_Course_2__Sample_Based_Learning_Methods_Learning_Objectives.pdf
02_meet-your-instructors.en.srt
02_meet-your-instructors.en.txt
01_course-introduction.en.srt
04_read-me-pre-requisites-and-learning-objectives_instructions.html
03_reinforcement-learning-textbook_instructions.html
01_course-introduction.en.txt
03_reinforcement-learning-textbook_RLbook2018.pdf
02_meet-your-instructors.mp4
01_course-introduction.mp4
02_monte-carlo-methods-for-prediction-control
04_off-policy-learning-for-prediction
04_emma-brunskill-batch-reinforcement-learning.en.srt
04_emma-brunskill-batch-reinforcement-learning.en.txt
03_off-policy-monte-carlo-prediction.en.srt
02_importance-sampling.en.srt
01_why-does-off-policy-learning-matter.en.srt
05_week-1-summary.en.srt
03_off-policy-monte-carlo-prediction.en.txt
01_why-does-off-policy-learning-matter.en.txt
02_importance-sampling.en.txt
05_week-1-summary.en.txt
06_chapter-summary_instructions.html
06_chapter-summary_RLbook2018.pdf
04_emma-brunskill-batch-reinforcement-learning.mp4
01_why-does-off-policy-learning-matter.mp4
03_off-policy-monte-carlo-prediction.mp4
05_week-1-summary.mp4
02_importance-sampling.mp4
01_introduction-to-monte-carlo-methods
04_using-monte-carlo-for-prediction.en.srt
03_what-is-monte-carlo.en.srt
03_what-is-monte-carlo.en.txt
04_using-monte-carlo-for-prediction.en.txt
01_module-1-learning-objectives_instructions.html
02_weekly-reading_instructions.html
02_weekly-reading_RLbook2018.pdf
04_using-monte-carlo-for-prediction.mp4
03_what-is-monte-carlo.mp4
03_exploration-methods-for-monte-carlo
01_epsilon-soft-policies.en.srt
01_epsilon-soft-policies.en.txt
01_epsilon-soft-policies.mp4
02_monte-carlo-for-control
03_solving-the-blackjack-example.en.srt
01_using-monte-carlo-for-action-values.en.srt
02_using-monte-carlo-methods-for-generalized-policy-iteration.en.srt
03_solving-the-blackjack-example.en.txt
01_using-monte-carlo-for-action-values.en.txt
02_using-monte-carlo-methods-for-generalized-policy-iteration.en.txt
03_solving-the-blackjack-example.mp4
01_using-monte-carlo-for-action-values.mp4
02_using-monte-carlo-methods-for-generalized-policy-iteration.mp4
03_temporal-difference-learning-methods-for-prediction
02_advantages-of-td
03_andy-barto-and-rich-sutton-more-on-the-history-of-rl.en.srt
01_the-advantages-of-temporal-difference-learning.en.srt
02_comparing-td-and-monte-carlo.en.srt
03_andy-barto-and-rich-sutton-more-on-the-history-of-rl.en.txt
01_the-advantages-of-temporal-difference-learning.en.txt
02_comparing-td-and-monte-carlo.en.txt
04_week-2-summary.en.srt
04_week-2-summary.en.txt
03_andy-barto-and-rich-sutton-more-on-the-history-of-rl.mp4
02_comparing-td-and-monte-carlo.mp4
01_the-advantages-of-temporal-difference-learning.mp4
04_week-2-summary.mp4
01_introduction-to-temporal-difference-learning
04_rich-sutton-the-importance-of-td-learning.en.srt
03_what-is-temporal-difference-td-learning.en.srt
04_rich-sutton-the-importance-of-td-learning.en.txt
03_what-is-temporal-difference-td-learning.en.txt
01_module-2-learning-objectives_instructions.html
02_weekly-reading_instructions.html
02_weekly-reading_RLbook2018.pdf
04_rich-sutton-the-importance-of-td-learning.mp4
03_what-is-temporal-difference-td-learning.mp4
05_planning-learning-acting
04_dealing-with-inaccurate-models
03_drew-bagnell-self-driving-robotics-and-model-based-rl.en.srt
02_in-depth-with-changing-environments.en.srt
01_what-if-the-model-is-inaccurate.en.srt
03_drew-bagnell-self-driving-robotics-and-model-based-rl.en.txt
02_in-depth-with-changing-environments.en.txt
01_what-if-the-model-is-inaccurate.en.txt
04_week-4-summary.en.srt
04_week-4-summary.en.txt
06_text-book-part-1-summary_instructions.html
05_chapter-summary_instructions.html
05_chapter-summary_RLbook2018.pdf
06_text-book-part-1-summary_RLbook2018.pdf
03_drew-bagnell-self-driving-robotics-and-model-based-rl.mp4
02_in-depth-with-changing-environments.mp4
01_what-if-the-model-is-inaccurate.mp4
04_week-4-summary.mp4
03_dyna-as-a-formalism-for-planning
02_the-dyna-algorithm.en.srt
01_the-dyna-architecture.en.srt
03_dyna-q-learning-in-a-simple-maze.en.srt
01_the-dyna-architecture.en.txt
03_dyna-q-learning-in-a-simple-maze.en.txt
02_the-dyna-algorithm.en.txt
02_the-dyna-algorithm.mp4
03_dyna-q-learning-in-a-simple-maze.mp4
01_the-dyna-architecture.mp4
01_what-is-a-model
03_what-is-a-model.en.srt
03_what-is-a-model.en.txt
04_comparing-sample-and-distribution-models.en.srt
01_module-4-learning-objectives_instructions.html
04_comparing-sample-and-distribution-models.en.txt
02_weekly-reading_instructions.html
02_weekly-reading_RLbook2018.pdf
03_what-is-a-model.mp4
04_comparing-sample-and-distribution-models.mp4
02_planning
01_random-tabular-q-planning.en.srt
01_random-tabular-q-planning.en.txt
01_random-tabular-q-planning.mp4
05_course-wrap-up
01_congratulations.en.srt
01_congratulations.en.txt
01_congratulations.mp4
04_temporal-difference-learning-methods-for-control
02_off-policy-td-control-q-learning
03_how-is-q-learning-off-policy.en.srt
02_q-learning-in-the-windy-grid-world.en.srt
01_what-is-q-learning.en.srt
03_how-is-q-learning-off-policy.en.txt
02_q-learning-in-the-windy-grid-world.en.txt
01_what-is-q-learning.en.txt
03_how-is-q-learning-off-policy.mp4
01_what-is-q-learning.mp4
02_q-learning-in-the-windy-grid-world.mp4
01_td-for-control
03_sarsa-gpi-with-td.en.srt
04_sarsa-in-the-windy-grid-world.en.srt
03_sarsa-gpi-with-td.en.txt
01_module-3-learning-objectives_instructions.html
04_sarsa-in-the-windy-grid-world.en.txt
02_weekly-reading_instructions.html
02_weekly-reading_RLbook2018.pdf
03_sarsa-gpi-with-td.mp4
04_sarsa-in-the-windy-grid-world.mp4
03_expected-sarsa
01_expected-sarsa.en.srt
02_expected-sarsa-in-the-cliff-world.en.srt
03_generality-of-expected-sarsa.en.srt
01_expected-sarsa.en.txt
04_week-3-summary.en.srt
02_expected-sarsa-in-the-cliff-world.en.txt
04_week-3-summary.en.txt
03_generality-of-expected-sarsa.en.txt
05_chapter-summary_instructions.html
05_chapter-summary_RLbook2018.pdf
01_expected-sarsa.mp4
02_expected-sarsa-in-the-cliff-world.mp4
03_generality-of-expected-sarsa.mp4
04_week-3-summary.mp4
06_Resources
01_notebook-grading-faqs
01__resources.html
prediction-control-function-approximation
01_welcome-to-the-course
01_course-introduction
03_read-me-pre-requisites-and-learning-objectives_Prediction_and_Control_with_Function_Approximation_Learning_
02_meet-your-instructors.en.srt
01_course-3-introduction.en.srt
02_meet-your-instructors.en.txt
01_course-3-introduction.en.txt
04_reinforcement-learning-textbook_instructions.html
03_read-me-pre-requisites-and-learning-objectives_instructions.html
04_reinforcement-learning-textbook_RLbook2018.pdf
02_meet-your-instructors.mp4
01_course-3-introduction.mp4
04_control-with-approximation
03_average-reward
02_satinder-singh-on-intrinsic-rewards.en.srt
01_average-reward-a-new-way-of-formulating-control-problems.en.srt
02_satinder-singh-on-intrinsic-rewards.en.txt
01_average-reward-a-new-way-of-formulating-control-problems.en.txt
03_week-3-review.en.srt
03_week-3-review.en.txt
02_satinder-singh-on-intrinsic-rewards.mp4
01_average-reward-a-new-way-of-formulating-control-problems.mp4
03_week-3-review.mp4
01_episodic-sarsa-with-function-approximation
04_episodic-sarsa-in-mountain-car.en.srt
03_episodic-sarsa-with-function-approximation.en.srt
04_episodic-sarsa-in-mountain-car.en.txt
05_expected-sarsa-with-function-approximation.en.srt
03_episodic-sarsa-with-function-approximation.en.txt
01_module-3-learning-objectives_instructions.html
02_weekly-reading-on-policy-control-with-approximation_instructions.html
05_expected-sarsa-with-function-approximation.en.txt
02_weekly-reading-on-policy-control-with-approximation_RLbook2018.pdf
03_episodic-sarsa-with-function-approximation.mp4
04_episodic-sarsa-in-mountain-car.mp4
05_expected-sarsa-with-function-approximation.mp4
02_exploration-under-function-approximation
01_exploration-under-function-approximation.en.srt
01_exploration-under-function-approximation.en.txt
01_exploration-under-function-approximation.mp4
03_constructing-features-for-prediction
03_training-neural-networks
03_david-silver-on-deep-learning-rl-ai.en.srt
01_gradient-descent-for-training-neural-networks.en.srt
03_david-silver-on-deep-learning-rl-ai.en.txt
02_optimization-strategies-for-nns.en.srt
01_gradient-descent-for-training-neural-networks.en.txt
02_optimization-strategies-for-nns.en.txt
04_week-2-review.en.srt
04_week-2-review.en.txt
03_david-silver-on-deep-learning-rl-ai.mp4
01_gradient-descent-for-training-neural-networks.mp4
02_optimization-strategies-for-nns.mp4
04_week-2-review.mp4
01_feature-construction-for-linear-methods
04_generalization-properties-of-coarse-coding.en.srt
06_using-tile-coding-in-td.en.srt
05_tile-coding.en.srt
04_generalization-properties-of-coarse-coding.en.txt
03_coarse-coding.en.srt
06_using-tile-coding-in-td.en.txt
02_weekly-reading-on-policy-prediction-with-approximation-ii_instructions.h
05_tile-coding.en.txt
01_module-2-learning-objectives_instructions.html
03_coarse-coding.en.txt
02_weekly-reading-on-policy-prediction-with-approximation-ii_RLbook2018.pdf
06_using-tile-coding-in-td.mp4
04_generalization-properties-of-coarse-coding.mp4
03_coarse-coding.mp4
05_tile-coding.mp4
02_neural-networks
02_non-linear-approximation-with-neural-networks.en.srt
03_deep-neural-networks.en.srt
01_what-is-a-neural-network.en.srt
02_non-linear-approximation-with-neural-networks.en.txt
01_what-is-a-neural-network.en.txt
03_deep-neural-networks.en.txt
03_deep-neural-networks.mp4
02_non-linear-approximation-with-neural-networks.mp4
01_what-is-a-neural-network.mp4
05_policy-gradient
04_policy-parameterizations
03_gaussian-policies-for-continuous-actions.en.srt
02_demonstration-with-actor-critic.en.srt
04_week-4-summary.en.srt
03_gaussian-policies-for-continuous-actions.en.txt
01_actor-critic-with-softmax-policies.en.srt
02_demonstration-with-actor-critic.en.txt
01_actor-critic-with-softmax-policies.en.txt
04_week-4-summary.en.txt
02_demonstration-with-actor-critic.mp4
03_gaussian-policies-for-continuous-actions.mp4
01_actor-critic-with-softmax-policies.mp4
04_week-4-summary.mp4
01_learning-parameterized-policies
03_learning-policies-directly.en.srt
04_advantages-of-policy-parameterization.en.srt
03_learning-policies-directly.en.txt
04_advantages-of-policy-parameterization.en.txt
01_module-4-learning-objectives_instructions.html
02_weekly-reading-policy-gradient-methods_instructions.html
02_weekly-reading-policy-gradient-methods_RLbook2018.pdf
04_advantages-of-policy-parameterization.mp4
03_learning-policies-directly.mp4
02_policy-gradient-for-continuing-tasks
02_the-policy-gradient-theorem.en.srt
01_the-objective-for-learning-policies.en.srt
02_the-policy-gradient-theorem.en.txt
01_the-objective-for-learning-policies.en.txt
01_the-objective-for-learning-policies.mp4
02_the-policy-gradient-theorem.mp4
03_actor-critic-for-continuing-tasks
02_actor-critic-algorithm.en.srt
01_estimating-the-policy-gradient.en.srt
02_actor-critic-algorithm.en.txt
01_estimating-the-policy-gradient.en.txt
02_actor-critic-algorithm.mp4
01_estimating-the-policy-gradient.mp4
05_course-wrap-up
01_congratulations-course-4-preview.en.srt
01_congratulations-course-4-preview.en.txt
01_congratulations-course-4-preview.mp4
02_on-policy-prediction-with-approximation
03_the-objective-for-td
03_doina-precup-building-knowledge-for-ai-agents-with-reinforcement-learning.en.srt
02_comparing-td-and-monte-carlo-with-state-aggregation.en.srt
03_doina-precup-building-knowledge-for-ai-agents-with-reinforcement-learning.en.txt
01_semi-gradient-td-for-policy-evaluation.en.srt
01_semi-gradient-td-for-policy-evaluation.en.txt
02_comparing-td-and-monte-carlo-with-state-aggregation.en.txt
03_doina-precup-building-knowledge-for-ai-agents-with-reinforcement-learning.mp4
01_semi-gradient-td-for-policy-evaluation.mp4
02_comparing-td-and-monte-carlo-with-state-aggregation.mp4
01_estimating-values-functions-with-supervised-learning
03_moving-to-parameterized-functions.en.srt
04_generalization-and-discrimination.en.srt
05_framing-value-estimation-as-supervised-learning.en.srt
03_moving-to-parameterized-functions.en.txt
04_generalization-and-discrimination.en.txt
02_weekly-reading-on-policy-prediction-with-approximation_i
01_module-1-learning-objectives_instructions.html
05_framing-value-estimation-as-supervised-learning.en.txt
02_weekly-reading-on-policy-prediction-with-approximation_R
03_moving-to-parameterized-functions.mp4
04_generalization-and-discrimination.mp4
05_framing-value-estimation-as-supervised-learning.mp4
02_the-objective-for-on-policy-prediction
04_state-aggregation-with-monte-carlo.en.srt
02_introducing-gradient-descent.en.srt
03_gradient-monte-for-policy-evaluation.en.srt
01_the-value-error-objective.en.srt
04_state-aggregation-with-monte-carlo.en.txt
02_introducing-gradient-descent.en.txt
03_gradient-monte-for-policy-evaluation.en.txt
01_the-value-error-objective.en.txt
04_state-aggregation-with-monte-carlo.mp4
03_gradient-monte-for-policy-evaluation.mp4
02_introducing-gradient-descent.mp4
01_the-value-error-objective.mp4
04_linear-td
02_the-true-objective-for-td.en.srt
03_week-1-summary.en.srt
01_the-linear-td-update.en.srt
02_the-true-objective-for-td.en.txt
03_week-1-summary.en.txt
01_the-linear-td-update.en.txt
03_week-1-summary.mp4
02_the-true-objective-for-td.mp4
01_the-linear-td-update.mp4
TutsNode.net.txt
.pad
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
[TGx]Downloaded from torrentgalaxy.to .txt
Torrent description
Feel free to post any comments about this torrent, including links to Subtitle, samples, screenshots, or any other relevant information, Watch Reinforcement Learning Specialization Online Free Full Movies Like 123Movies, Putlockers, Fmovies, Netflix or Download Direct via Magnet Link in Torrent Details.
