udacity/deep-reinforcement-learning

Features

• RL Agent Implementation Frameworks - Provides a structural framework for building reference versions of value-based and policy-based reinforcement learning agents.
• Reinforcement Learning Curricula - Provides a structured learning path and implementation exercises specifically for the study of reinforcement learning.
• Actor-Critic Architectures - Implements architectures that combine policy-based agents with value-based evaluators to balance exploration and exploitation.
• Deterministic Policy Gradients - Implements methods for optimizing continuous actions by predicting deterministic values based on critic network gradients.
• Deep Q-Learning Implementations - Implements deep Q-learning algorithms using neural networks to estimate optimal action-value functions.
• DDPG Implementations - Implements the Deep Deterministic Policy Gradient algorithm for solving continuous control tasks in physics environments.
• Policy Gradient Implementations - Provides a framework for optimizing decision-making policies using gradient-based methods like REINFORCE.
• Policy Gradient Optimizers - Implements gradient-based methods for updating policy parameters to maximize expected rewards in various action spaces.
• REINFORCE Implementations - Implements the REINFORCE algorithm to optimize neural network action probabilities using episodic reward accumulation.
• Neural Action-Value Estimation - Uses neural networks to approximate action-value functions for complex state spaces in simulated environments.
• Agent Implementations - Provides practical code implementations for building autonomous agents that select actions and learn from experience.
• Temporal Difference Learning - Implements algorithms that update value estimates incrementally by comparing current predictions with subsequent observations.
• Multi-Agent Training - Provides training frameworks that coordinate multiple agents to cooperate or compete within shared simulated environments.
• Continuous Control Training - Implements training for agents in high-dimensional, real-valued action spaces typical of physics-based simulations.
• Dynamic Programming Solvers - Includes solvers for Markov Decision Processes using value and policy iteration through dynamic programming.
• Agent Performance Evaluators - Implements tools for assessing agent behavior and policy stability by calculating average and peak returns over thousands of episodes.
• Markov Decision Process Solvers - Implements algorithms for calculating optimal action sequences in environments where future states depend on current actions.
• Monte Carlo Sampling Methods - Implements techniques for estimating state values by averaging accumulated rewards from complete interaction episodes.
• Monte Carlo Trajectory Estimators - Implements Monte Carlo methods to estimate expected returns by averaging cumulative rewards from complete episodes.
• Cross-Entropy Method Implementations - Implements an optimization approach for policies based on high-reward trajectories to solve continuous control environments.
• Parallel Experience Collection - Provides the capability to execute multiple identical simulation instances simultaneously to accelerate training data gathering.
• Reinforcement Learning Trading Frameworks - Provides a framework for training agents that use reinforcement learning to analyze market data and execute asset trading.
• Reinforcement Learning Training - Provides general frameworks for training agents to solve control problems within virtual scenarios and simulations.
• Multi-Agent Training - Implements architectures for coordinating multiple specialized agents to cooperate or compete within shared environments.
• Navigation Agent Training - Includes implementations for teaching virtual agents to move through spatial environments using perception data.
• Learning and Reference - Deep RL Nanodegree repo.

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