Dr. Thomas Starke – Deep Reinforcement Learning in Trading

• List and explain the need for reinforcement learning to tackle the delayed gratification experiment
• Describe states, actions, double Q-learning, policy, experience replay and rewards.
• Explain exploitation vs exploration tradeoff
• Create and backtest a reinforcement learning model
• Analyse returns and risk using different performance measures
• Practice the concepts on real market data through a capstone project
• Explain the challenges faced in live trading and list the solutions for them
• Deploy the RL model for paper and live trading

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