Reinforcement Learning Techniques
( 25 Modules )

Module #1
Introduction to Reinforcement Learning
Overview of RL, its applications, and key concepts

Module #2
Markov Decision Processes
Mathematical framework for RL, MDPs, and value functions

Module #3
Value-Based Methods
Introduction to value-based methods, including value iteration and policy iteration

Module #4
Policy Gradient Methods
Introduction to policy gradient methods, including REINFORCE and actor-critic methods

Module #5
Model-Based Methods
Introduction to model-based methods, including model-based RL and Dyna-Q

Module #6
Q-Learning
In-depth exploration of Q-learning, including its variants and applications

Module #7
SARSA
In-depth exploration of SARSA, including its variants and applications

Module #8
Deep Q-Networks (DQN)
Introduction to DQN and its applications in deep RL

Module #9
Deep Deterministic Policy Gradient (DDPG)
Introduction to DDPG and its applications in continuous control

Module #10
Actor-Critic Methods
In-depth exploration of actor-critic methods, including A2C and A3C

Module #11
Policy Gradient Theorem
Mathematical derivation of the policy gradient theorem

Module #12
Entropy-Regularized RL
Introduction to entropy-regularized RL and its applications

Module #13
Exploration-Exploitation Tradeoff
Strategies for balancing exploration and exploitation in RL

Module #14
Multi-Agent RL
Introduction to multi-agent RL and its applications

Module #15
Imitation Learning
Introduction to imitation learning and its applications in RL

Module #16
RL Environments
Introduction to popular RL environments, including Gym and Mujoco

Module #17
RL Algorithms for Robotics
Introduction to RL algorithms specifically designed for robotics

Module #18
RL for Game Playing
Introduction to RL algorithms for game playing, including AlphaGo and AlphaZero

Module #19
RL for Recommendation Systems
Introduction to RL algorithms for recommendation systems

Module #20
Trust Region Policy Optimization (TRPO)
Introduction to TRPO and its applications in RL

Module #21
Proximal Policy Optimization (PPO)
Introduction to PPO and its applications in RL

Module #22
RL for Real-World Applications
Case studies of RL applications in real-world domains

Module #23
RL and Neuroscience
Introduction to the connection between RL and neuroscience

Module #24
RL and Ethics
Discussion of ethical considerations in RL, including fairness and accountability

Module #25
Course Wrap-Up & Conclusion
Planning next steps in Reinforcement Learning Techniques career

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Reinforcement Learning Techniques ( 25 Modules )

Reinforcement Learning Techniques
( 25 Modules )