Reinforcement Learning with Pytorch

Explore why reinforcement learning and deep learning rise in popularity as Moore's law drives exponential computing power growth and cheaper technology, fueling AI progress.

Explore reinforcement learning with pytorch through self-play and self-training breakthroughs like alpha go zero. See practical cases, popular algorithms, and open gym tools to experiment freely.

Learn reinforcement learning with concepts of agent, environment, observations, actions, and rewards, including delayed feedback, and compare with supervised and unsupervised learning.

Prepare your environment for reinforcement learning with PyTorch by detailing Linux installation steps and Depue drivers installation, and note that installation processes change over time.

Practice with the frozen lake version zero environment in OpenAI Gym, where the agent navigates a four-square grid with holes toward a goal, observes random actions, and begins learning.

Learn how to record and visualize your reinforcement learning agent's behavior in OpenAI Gym environments, including headless server video setup and essential installation steps.

Explore the Bellman equation by breaking down the q value function, policy, and gamma-discounted rewards, and learn how to evaluate state-action pairs to find the optimal value.

Learn how Bellman equations guide reinforcement learning by updating q-values to maximize future rewards, while exploring the frozen lake environment with four actions and learning from state and action interactions.

In a deterministic environment, define a 16x4 Q-table, initialize with zeros, and update it with the Bowman equation using gamma to select the best actions.

Use thought Max to extract the maximum value and its index from muxing values, guiding the best move for each state in a deterministic environment.

Implement a reinforcement learning lab in PyTorch for deterministic environments, building q-values, random action selection, state updates, and performance reporting with rewards visualization across multiple runs.

Visualize reinforcement learning results for deterministic environments with matplotlib bar graphs, configuring x and y axes and colors, and report episodes success rates and average steps.

Explore reinforcement learning in a stochastic 'frozen lake' environment with PyTorch. Document results, analyze why Bellman equation struggles in stochastic settings, and compare to deterministic cases.

Explore q-learning in stochastic environments, balancing current observations with past experience using temporal difference and alpha to update action-value estimates.

Apply a stochastic environment reinforcement learning lab by adapting the Q-learning update with a learning rate alpha and discount gamma, balancing current observations and past experience to improve policy.

Explore the exploitation vs exploration dilemma in reinforcement learning. Use an epsilon-greedy approach to balance random exploration with choosing the best-known action, and adjust epsilon during training.

Implement epsilon-greedy action selection in a PyTorch reinforcement learning lab, balancing 10% random exploration with 90% optimized moves, and analyze learning improvements and gamma effects.

Implement adaptive e-greedy by starting with a high exploration rate around 0.7 and decaying toward 0.1, with a greedy decay of 0.999. This approach reduces random moves over time.

Solve the taxi environment homework by building a from-scratch reinforcement learning agent that picks up and drops off a passenger across four locations, earning 20 points with a per-step penalty.

Explore tuning reinforcement learning parameters in a PyTorch homework setting, comparing gamma, alpha, and exploration rate to observe effects on learning from current observations, experience, and deterministic environments.

Learn to run reinforcement learning experiments with the CartPole environment, seed randomness for reproducibility, and compare simple algorithms on a standard laptop, observing an average of about 22 steps.

The video revisits epsilon decay in reinforcement learning with PyTorch, showing how to visualize and compare two epsilon calculation methods and adjust decay parameters.

Learn to migrate to PyTorch 0.4.0, implement device-agnostic code, and adapt to updated data specifications and deprecated features, with practical script updates and remote plotting considerations.

Implement a neural network for an OpenAI Gym environment, define inputs from observation space and actions, and build an agent with select_action and optimize to learn via the Bohman equation.

Build a PyTorch-based agent for OpenAI Gym that uses q-learning: compute target as reward plus gamma times max next-state value, detach grads, predict q-values for actions, and update the network.

Implement and tune an epsilon-greedy exploration schedule for a neural network agent in OpenAI Gym, calculating epsilon from total steps and adjusting gamma across more episodes to document improvements.

Extend logging in the reinforcement learning with pytorch lab to track episode counts, averages, epsilon, elapsed time, and solve status using configurable report intervals.

Explore how deep reinforcement learning, via deep q-networks, solves complex Atari games and surpasses hand-crafted features, fueled by advances in deep learning and computer vision.

Experiment with a deeper neural network by adding a second hidden layer of 64 units, using activation between layers, achieving higher average rewards around 150 and approaching 200 steps.

Tune reinforcement learning parameters to observe their impact on results, adjusting the learning rate and other settings. Set clear goals to prioritize speed or stable performance.

Explore how highly correlated data and non-stationary distributions hinder learning, and show how experience replay uses a memory buffer to sample random transitions, guiding updates to the Q-function.

Implement an experience replay memory for reinforcement learning in PyTorch by building a memory class with push, sample, and __len__ methods, handling capacity and overwriting old entries.

Learn to implement experience replay in PyTorch, align predicted and target values with tensor squeeze, and evaluate performance in a CartPole environment, achieving stable improvements.

Explore how the deep q-network (dqn) achieves human-level control on Atari games using experience replay, a fixed target network, and stable updates to q-values, with clipping for stability.

Implement a PyTorch DQN lab with a target network and a 500-step update cadence. Load target parameters from the main network and tune gamma and clipping for stability.

Implement a double DQN using a learning network to pick actions and a target network to evaluate their values, with double dip Q and stabilized target frequency.

Explore the dueling network architecture for deep Q-learning, separating state value and state-dependent action advantages into two streams. See how combining them improves efficiency and stability on Atari tasks.

Investigate stability-focused dueling DQN experiments, compare two model variants, tune parameters and update frequency, and extend the network with deeper value and advantage streams to achieve stable 200-point performance.

Learns from video output using convolutional neural networks to analyze individual frames; reviews cnn architecture with convolution, pooling, and fully connected layers for visual recognition.

Build a pong agent that learns from video output frames using value calculations, run on a headless server, with save/load mechanics and a three-conv, two-fc cnn architecture.

Develop a convolutional network in pytorch for a Pong agent, detailing conv layers, flattening, and outputs for advantage and value in reinforcement learning.

Debug and preprocess environment image data for Pong, inspecting state, dtype, and shape; convert frames via transpose and preprocess_frame to produce a 4d float tensor suitable for the PyTorch model.

Preprocess and stack 42 Pong frames from experience replay into 84×84 tensors, convert to float, and verify max action scores from the neural network to drive decisions.

Experiment with potential improvements in reinforcement learning by stacking four frames with Atari wrappers, adjusting CNN input channels, tuning hyperparameters, and exploring rainbow and experience replay methods.

Dive into reinforcement learning with PyTorch, and practice building a learning agent from scratch, tuning results, and adapting code as framework updates roll out.