yandexdataschool/Practical_RL
Practical RL
Practical_RL is a comprehensive educational curriculum and course for learning to design and implement agents that solve complex decision processes. It provides a structured study program covering the fundamentals of reinforcement learning, from basic trial-and-error behavior to advanced deep reinforcement learning.
The project includes specialized guides and frameworks for imitation learning based on expert demonstrations, model-based reinforcement learning using planners, and the training of recurrent neural networks to solve partially observed environments.
The materials cover a broad range of machine learning capabilities, including the implementation of value-based and policy-gradient algorithms, the design of exploration strategies, and the construction of deep learning foundations for continuous state spaces.
The content is delivered primarily through Jupyter Notebooks.
Features
- Deep Reinforcement Learning Implementations - Provides functional implementations of advanced reinforcement learning agents using deep neural networks.
- Reinforcement Learning Implementations - Implements a wide range of reinforcement learning agents, including value-based, model-free, and policy-gradient algorithms.
- Experience Replay Buffers - Implements memory structures that store agent transitions to break temporal correlations during neural network training.
- Expert Imitation Learning - Implements frameworks for training agents to mimic expert demonstrations using inverse reinforcement learning and behavior cloning.
- Policy and Value Function Approximators - Constructs neural network architectures specifically to estimate values and action probabilities in reinforcement learning.
- Policy Gradient Methods - Provides gradient-based architectures for updating policy parameters in discrete and continuous action spaces.
- Partially Observable MDP Solvers - Provides architectures using recurrent neural networks to solve decision processes in environments with hidden state information.
- Deep Learning Foundations - Provides core frameworks and educational resources for machine learning and neural networks.
- Reinforcement Learning Curricula - Provides structured learning paths specifically for the study of reinforcement learning.
- Behavioral Agent Training Environments - Provides a training environment for solving complex tasks through trial and error across robotics and finance domains.
- Imitation Learning Guides - Provides materials for developing behavioral policies based on expert demonstrations and inverse reinforcement learning techniques.
- Exploration Strategies - Provides tools and implementations for balancing exploration and exploitation using methods like Thompson Sampling and Monte Carlo Tree Search.
- Model-Based Reinforcement Learning Frameworks - Provides a guide to designing exploration strategies and planners using Monte Carlo Tree Search and Thompson Sampling.
- Monte Carlo Tree Search - Implements a search algorithm that uses random sampling of the game tree to determine optimal moves.
- Recurrent Neural Network Training - Provides frameworks for building and training recurrent networks with parallelized computational efficiencies.
- Memory Architectures for Partially Observable MDPs - Integrates hidden state tracking via recurrent layers to handle partially observable environments and temporal dependencies.
- Target Network Decoupling - Uses a separate, slowly updating set of weights to prevent divergence during value function approximation.
