Pendulum Learning Lab

Project overview

Learning control through trial, reward and evaluation.

A reinforcement-learning lab comparing Standard, Double, Dueling and Rainbow DQN on Pendulum-v1. Continuous torque is mapped to discrete actions, with reusable replay, training, evaluation and tuning components. Local smoke execution is kept distinct from full studies; matched multi-seed reward superiority and control stability are not claimed.

• Reinforcement Learning
• DQN
• Python
• Optimisation

Inside this build

• Mapped continuous torque to discrete action bins for value-based learning.
• Configured seeded experiments with replay buffers, warm-up and target-network updates.
• Added prioritised replay and distributional outputs for Rainbow DQN.
• Provided reward summaries, learning-curve analysis, greedy rollouts and comparison views.
• Separated bounded smoke runs from the full 500-episode study configuration.
• Validated experiment tooling and notebook integrity while documenting missing matched multi-seed runs.

Source code & links