simoninithomas/Deep_reinforcement_learning_Course
Deep Reinforcement Learning Course
This repository serves as an educational curriculum for learning deep reinforcement learning through structured, hands-on coding exercises. It provides a framework for building and training autonomous agents that learn to perform tasks by interacting with simulated environments and receiving iterative feedback.
The project covers the implementation of decision-making models using deep neural function approximation, temporal difference learning, and gradient-based policy optimization. It emphasizes the use of experience replay buffering and vectorized environment simulation to stabilize training and accelerate data collection within Markov decision processes.
Beyond core training techniques, the material includes resources for evaluating agent performance against standardized benchmarks and sharing trained models within a collaborative research context. The repository is designed for use with common machine learning frameworks, providing a comprehensive set of tutorials and implementations for developing intelligent agents.
Features
- Reinforcement Learning Curricula - Offers a structured curriculum for learning deep reinforcement learning through hands-on coding exercises.
- Reinforcement Learning Training Loops - Provides structured training loops for reinforcement learning agents, including state-transition recording and experience replay.
- Machine Learning Curricula - Delivers a structured curriculum for mastering reinforcement learning techniques via coding exercises and performance benchmarking.
- Autonomous Agent Simulations - Provides simulated environments for training and analyzing the behavior of autonomous agents.
- Deep Learning Training Toolsets - Serves as a framework for building and testing decision-making models through iterative feedback loops.
- Deep Reinforcement Learning Implementations - Collects tutorials and code implementations for training autonomous agents using deep learning frameworks.
- Experience Replay Buffers - Utilizes memory buffers to store and sample past transitions, breaking temporal correlations to stabilize the training process.
- Reinforcement Learning Model Sharing - Upload and share completed agent files to a central repository so other developers can access, test, and build upon your work for collaborative research.
- Policy and Value Function Approximators - Implements neural network architectures to estimate action probabilities and value functions for reinforcement learning agents.
- Policy Gradient Optimizers - Optimizes agent policy parameters using gradient-based updates to maximize cumulative rewards during training.
- Vectorized Environments - Accelerates data collection by running multiple parallel simulation instances for training reinforcement learning agents.
- Temporal Difference Learning - Updates value functions by iteratively calculating the difference between predicted outcomes and observed feedback.
- Reinforcement Learning Evaluation - Provides resources for benchmarking reinforcement learning models against standardized challenges.
- Agent Performance Benchmarks - Evaluates agent performance against standardized benchmarks and competitive leaderboards to identify effective solutions.
