Advanced AI: Deep Reinforcement Learning in Python

Explore the theory of reinforcement learning, contrasting it with supervised learning, emphasizing time, goals, and future planning. The lecture highlights data labeling challenges using self-driving cars and mazes.

Define the agent and environment in reinforcement learning, and illustrate episodes, states, actions, rewards, and state and action spaces via tic tac toe, breakout, and maze examples.

Represent states as discrete indices or continuous vectors, encode actions accordingly, and implement policies as probabilistic mappings with epsilon-greedy exploration to maximize rewards.

Define states, actions, and rewards within the agent–environment framework to form solvable Markov decision processes, and study the Markov assumption, state-transition probabilities, and Q-learning.

Maximize the sum of future rewards to enable long-term planning, using gamma discounting and the recursive return definition to evaluate future sequences.

Derive the Bellman equation from expected values to define the value function V_pi(s) as the gamma-discounted expected return. When policy and environment dynamics are known, solve V using linear algebra.

Define the control problem in reinforcement learning and contrast it with the prediction problem. Relate state value V(s) and action value Q(s, a) to the optimal policy and its evaluation.

Explore real reinforcement learning algorithms using Monte Carlo sampling to estimate state values under a policy, discuss prediction versus control problems, and derive returns from episodes.

Explore policy evaluation and improvement in reinforcement learning, using Monte Carlo methods to update state and action values (V and Q) through generalized policy iteration and exponentially decaying averages.

Implement epsilon-greedy to solve the explore-exploit dilemma in reinforcement learning by balancing exploration with greedy action selection based on Q-values.

Explore the Q-learning algorithm and its temporal difference updates, contrasting Monte Carlo methods with bootstrapped, off-policy learning using an epsilon-greedy policy to update a Q-table in reinforcement learning.

Learn why reinforcement learning differs from supervised learning, why you must implement algorithms yourself, and move from tabular methods to deep learning through dynamic programming, Monte Carlo, and temporal difference.

Submit feedback through a simple online suggestion box to help tailor this deep reinforcement learning course; share background, course difficulty, missing explanations, and future topic requests.

Learn OpenAI Gym basics by connecting to an environment, resetting to the start state, and running episodes with random actions to inspect observations, actions, rewards, and done signals.

Explore random search for linear policy optimization in a reinforcement learning task, evaluating 100 random weight vectors by average episode length to select the best performing parameters.

Learn how to save a video of an agent playing an episode by wrapping the environment with a monitor and specifying a save directory, then view the rendered video.

Explore how discretizing the CartPole state into finite bins enables tabular Q-learning, including handling states outside the box, choosing bin sizes, and shaping rewards to guide learning.

Apply q-learning with discrete, quantized CartPole states to build a q-table, including a feature transformer, state binning, epsilon-greedy action selection, and running-average performance over episodes.

Examine radial basis function networks for function approximation in deep reinforcement learning, using Gaussian kernels with exemplars and a linear model or a single hidden layer.

Implement q-learning with an rbf network to solve the mountain car problem, using a feature transformer and multiple regressors. Apply epsilon-greedy exploration, partial_fit, and optimistic initial values for stable learning.

Modify the RBF network to work on cartpole instead of mountain car. Implement partial_fit and predict to mirror a gradient-descent model and design plausible state ranges for better RBF exemplars.

Implement q-learning with an rbf network to solve CartPole, using a feature transformer, standard scaler, epsilon-greedy training, and reward tracking with running averages.

This lecture re-implements a simple neural network training loop in Theano, highlighting graph inputs, shared parameters, cost function, updates, and training and prediction routines.

Practice a TensorFlow warmup for deep reinforcement learning by building a simple model with variables, placeholders, a squared-error cost, and an interactive session to train and predict.

Plug a neural network into the existing deep reinforcement learning script, explore non-linear models, discuss transfer learning, catastrophic forgetting, and dropout regularization with various architectures.

Explore OpenAI Gym environments and build reinforcement learning agents in Python, from random search to Q-learning with BF networks and linear function approximators, across mountain car and karpel tasks.

Apply n-step td lambda methods to balance one-step learning and full returns, using n-step returns to update v and q for prediction and control with greedy or epsilon policies.

Implement the step method for mountain car using an n-step Q-learning update, tracking recent states and rewards, applying a gamma-based multiplier, and handling gym termination at 200 steps.

Explore td lambda as a generalization of the end step method and the lambda return, using eligibility trace to blend step and Monte Carlo and control the tradeoff via lambda.

Learn to implement TD lambda in code for deep reinforcement learning, using eligibility traces, gamma and lambda updates, and a streamlined update path with a class-based model and gradient features.

Learn how td lambda generalizes one-step returns to end-step and Monte Carlo methods with eligibility traces, producing lambda returns. Relate this online update to momentum-like ideas from deep learning.

Explore policy gradient methods in deep reinforcement learning by parameterizing the policy with softmax, optimizing the policy objective via gradient ascent, and using baselines and actor-critic updates.

implement a policy gradient solution in TensorFlow for cartpole using a linear policy and a neural network value function; test architectures, calculate returns and advantages, and explore optimizer options.

Explore policy gradient methods in python using Theano to solve CartPole, reusing neural network architectures with advanced optimizers, and implementing policy and value models with gradient updates.

Extend policy gradient to continuous action spaces using a Gaussian with mean and variance as linear functions of features, with positive variance via exponential or softplus, and standard updates.

This lecture applies a parameterized policy to the continuous mountain car, explains the reward structure, and uses hill climbing to optimize parameters, with neural networks or radial basis function models.

Implement a parameterized policy for the continuous mountain car using Theano, detailing mean and variance models, Gaussian sampling, and hill climbing with perturbations and random search to maximize average reward.

Implement a parameterized policy for the continuous mountain car using hill climbing and random search, with a gaussian mean variance policy, TensorFlow utilities, and action clipping between -1 and 1.

Apply policy gradient with gradient ascent to train a continuous mountain car using a gaussian action distribution in TensorFlow v2, incorporating value-based advantages and entropy regularization.

Apply policy gradient methods to a continuous Mountain Car using a Gaussian policy, and train with gradient descent and entropy regularization to learn both policy and value models.

Learn policy gradient methods in deep reinforcement learning by modeling a probabilistic policy with learnable parameters and, for continuous action spaces, using a Gaussian with mean and variance.

Explore deep reinforcement learning with deep q-learning for complex games like Atari, highlighting how large state spaces, long training times, and costs shape practical experiments, and favoring CPU-based testing.

Explore deep Q-learning with a deep Q-network, experience replay, and a target network, using grayscale four-frame inputs through convolutional neural networks to stabilize training.

Implement deep Q-learning in TensorFlow for CartPole, building a DQN with a target network, experience replay, and parameter copying to observe learning in action.

Implement deep Q-learning in Theano for CartPole, using experience replay, a target network, and gamma-based Q targets. Tune hyperparameters and experiment with optimizers and batch sampling.

Explore practical Atari deep reinforcement learning details for Breakout. Crop and greyscale frames, downsample with nearest-neighbor interpolation, and use TensorFlow built-in layers and scopes for efficient networks and epsilon scheduling.

Learn a practical pseudocode for deep q-learning on Atari and implement an efficient experience replay memory with main and target networks, epsilon-greedy action selection, and regular weight updates.

Explore deep Q-learning in TensorFlow applied to Breakout, detailing replay memory, target network updates, image preprocessing (grayscale, crop, resize), state construction from four frames, and epsilon-greedy training.

Explore deep Q-learning in Theano for Breakout. Implement replay memory, grayscale downsampled 84x84 frames, four-frame state construction, convolutional layers, target network updates, and epsilon-greedy training to learn game policies.

Explore partially observable MDPs and learn how integrating sequences of observations with recurrent neural networks helps infer the true state and guide learning from time-based data.

Recaps deep Q-learning concepts, highlighting experience replay, target networks, and the RPF network, and shows CNN-based feature learning for Breakout, Pong, Space Invaders, Seaquest, and Beam Rider.

Explore asynchronous advantage actor-critic (a3c) with multiple parallel workers updating global policy and value networks, using n-step returns and entropy regularization to stabilize learning.

Explore Python threading with a simple A3C-style example where multiple workers increment a shared global counter to 20. Learn how thread-safe counters, worker IDs, and join synchronize completion.

Explore implementing A3C in Python by wiring value and policy networks, multiple workers, and threading with a tf coordinator, for Breakout with 5 million steps and smoothed return plots.

Explore building a shared feature extractor for a policy and value network in A3C, using a lean convolutional neural network, scaling inputs by 255, and handling four grayscale frames.

Explain the a3c worker, where each thread runs local networks, copies global weights, and updates them with local gradients, plus image preprocessing and four-frame state construction.

Learn how A3C achieves training stability through parallel workers and n-step returns, contrasting with DQN's replay buffer and target networks, and highlighting hyperparameter search challenges.

Summarize how deep learning integrates with reinforcement learning, covering MDPs, dynamic programming, Monte Carlo, and TD methods, policy gradient, deep q-learning with experience replay and target networks.

Explore Theano basics by defining scalar, vector, and tensor variables, creating shared variables, and building symbolic functions to compute gradients and train models.

Implement a Theano neural network in Python with a softmax output, a cost function and regularisation, and train and predict using data from previous examples.

Master TensorFlow basics by defining variables and placeholders, running sessions, performing matrix multiplication, and applying a gradient descent optimizer to minimize a simple cost function.

Build a TensorFlow neural network by adding a second hidden layer, configure placeholders and variables, apply softmax cross-entropy loss, train with an optimizer, and analyze error rates for overfitting.

Clarify the appendix as a FAQ with supplementary material beyond the main content. Note the FAQ is optional and urge using the Q&A to ask questions until resolved.

This pre-installation check clarifies that installation lectures are guidelines and emphasizes learning Python principles over syntax. It covers using pip to install libraries and choosing course-relevant tools like OpenAI Gym.

Install and manage data science libraries on Windows with Anaconda, isolating environments and setting up Python, NumPy stack, and frameworks like TensorFlow, PyTorch, Keras, and open gym.

Learn to set up a cross-platform deep learning environment across Windows, Linux, and Mac, and install NumPy, SciPy, Matplotlib, Pandas, IPython, Theano, and TensorFlow using virtual machines, pip, and conda.