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Course Outline
Introduction to Reinforcement Learning
- What is reinforcement learning?
- Key concepts: agent, environment, states, actions, and rewards
- Challenges in reinforcement learning
Exploration and Exploitation
- Balancing exploration and exploitation in RL models
- Exploration strategies: epsilon-greedy, softmax, and more
Q-Learning and Deep Q-Networks (DQNs)
- Introduction to Q-learning
- Implementing DQNs using TensorFlow
- Optimizing Q-learning with experience replay and target networks
Policy-Based Methods
- Policy gradient algorithms
- REINFORCE algorithm and its implementation
- Actor-critic methods
Working with OpenAI Gym
- Setting up environments in OpenAI Gym
- Simulating agents in dynamic environments
- Evaluating agent performance
Advanced Reinforcement Learning Techniques
- Multi-agent reinforcement learning
- Deep deterministic policy gradient (DDPG)
- Proximal policy optimization (PPO)
Deploying Reinforcement Learning Models
- Real-world applications of reinforcement learning
- Integrating RL models into production environments
Summary and Next Steps
Requirements
- Experience with Python programming
- Basic understanding of deep learning and machine learning concepts
- Knowledge of algorithms and mathematical concepts used in reinforcement learning
Audience
- Data scientists
- Machine learning practitioners
- AI researchers
28 Hours
Delivery Options
Private Group Training
Our identity is rooted in delivering exactly what our clients need.
- Pre-course call with your trainer
- Customisation of the learning experience to achieve your goals -
- Bespoke outlines
- Practical hands-on exercises containing data / scenarios recognisable to the learners
- Training scheduled on a date of your choice
- Delivered online, onsite/classroom or hybrid by experts sharing real world experience
Private Group Prices RRP from £7600 online delivery, based on a group of 2 delegates, £2400 per additional delegate (excludes any certification / exam costs). We recommend a maximum group size of 12 for most learning events.
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very friendly and helpful
Aktar Hossain - Unit4
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All good, nothing to improve
Ievgen Vinchyk - GE Medical Systems Polska Sp. Z O.O.
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