Reinforcement Learning: The Complete Course in 2022

Explore robot control systems and how deep reinforcement learning plans movements amid obstacles. Understand the three laws of robotics and how sensors and actuators enable autonomous, safe industrial robots.

Explore a concise robotics timeline from early automated devices to fourth generation robots, and examine how automatic control enables perception, interaction, and intelligent industrial and social robotics.

Explore reinforcement learning basics, where an agent learns from interaction with the environment through actions, rewards, and memory to maximize future rewards.

Explore reinforcement learning fundamentals, including agent rewards and penalties, policy evaluation and improvement, and how dynamic programming, Monte Carlo methods, and online learning estimate value functions within Markov decision processes.

Learn how Keras DQN applies Q-learning with deep neural networks to estimate value functions, improve policies, and update Q-values via practical pseudocode.

Implement a cart-pole system as an inverted pendulum in gym, using left or right actions (±1) to balance upright and earn per-step reward, ending when the angle exceeds 15 degrees.

The lecture continues cart pole reinforcement learning, detailing observation, environment, left-right actions, rewards, and episodic interactions.

Continue the cart pole project into testing, evaluate the observation and environment, and apply actions and rewards from the trained policy.

Explore reinforcement learning basics, environments, and how to apply a cartpole control using plus or minus one force to swing up and stabilize.

Develop and implement a policy gradient algorithm by sampling actions from a probabilistic policy, computing the gradient from episode rewards, and updating the agent within a simulated environment.

Demonstrate policy gradient concepts by running episodes, computing total rewards, and applying gradient updates with a learning rate of 0.001.

Develop a policy gradient approach by computing total and average rewards over episodes, plot performance, and update the policy via stochastic gradient using episode rewards and action probabilities.

Develop a policy gradient algorithm by setting up the environment, running episodes, and computing rewards, then track learning progress via average total reward over thousands of episodes.

Develop hill climbing in a reinforcement learning project by running parallel episodes with noisy exploration, updating action weights from rewards, and tracking total and best rewards across episodes.

Develop hill climbing experiments in reinforcement learning, adjust noise scale based on performance, evaluate episode rewards, and visualize total rewards to improve convergence.

Learn hill climbing in reinforcement learning by adding adaptive noise to each episode, like a gradient descent toward a target reward, improving total and average rewards toward the optimal value.

Explore reward dynamics in reinforcement learning through hill climbing, examining rewards peaking within the first 100 episodes and the search for an optimal policy amid noisy evaluations.

Explore simulating Atari 2600 environments with gym, including reset, step, actions, rewards, and done conditions, using alien invaders to illustrate reinforcement learning fundamentals.

Simulate Atari environments part 2 shows how the environment step maps actions to game moves and how render updates the observation. The agent controls a spaceship to shoot aliens.

Introductory session presents the Markov decision process and dynamic programming, guiding practical reinforcement learning with policy evaluation, value iteration, policy iteration, and the frozen environment example.

Explore the Markov property, build a transition matrix, and compute state distributions from an initial distribution using a study and sleep example.

Create a Markov chain to model study and sleep transitions, compute transition probabilities from a matrix, and analyze convergence to the long-run distribution after several steps.

Demonstrate how a Markov chain updates its state distribution across iterations, turning initial probabilities into evolving values like 0.52 and 0.48 and showing convergence.

Explore how a Markov chain evolves through the transition matrix and initial state distribution. Observe how the state distribution converges over iterations to a steady equilibrium, about 57.14% and 42.86%.

Introduce MDP concepts, transition matrices, actions (a0 and a1), and a reward function, illustrating how to derive the optimal policy and its value function, using matrix inversion.

We build and solve an MDP by defining a transition matrix, setting rewards and gamma, and computing the value function via matrix inversion to identify the optimal policy.

Explore reinforcement learning with MDP implementation, experimenting with gamma values 0, 0.9, and 0.99, and examine how rewards, the transition matrix, and the value function change under the optimal policy.

Learn how a Markov decision process underpins reinforcement learning, derive the optimal policy via the value function using the Bellman equation, and compute values with matrix inversion, noting scalability limits.

Explore policy evaluation by iteratively updating state values with the Bellman expectation equation until convergence, using an mvp to compute the optimal policy value function via matrix inversion.

Implement a policy evaluation algorithm to compute the value function using a transition matrix, rewards, and a discount factor, with a threshold to stop the evaluation.

Explore iterative policy evaluation in reinforcement learning, updating value functions via the Bellman expectation equation toward an optimal or random policy, and compare with matrix inversion.

Explore how policy evaluation estimates a policy's value through iterative updates guided by the Bellman expectation equation within dynamic programming, terminating when values converge.

Apply policy evaluation by recording a value for each iteration, build a history of values for the optimal policy, and analyze convergence and tradeoffs between immediate versus future rewards.

Explore simulating a frozen lake environment for reinforcement learning, using 4x4 and 8x8 grids with slippery ice and a 16-dimensional observation space. Test random policies and compare rewards across episodes.

Simulate the frozen lake environment on a discrete grid and explore its optimal policy. Use gym to create, reset, render, and step through episodes, noting random-policy rewards and slip effects.

Simulate the FrozenLake environment in reinforcement learning by running thousands of episodes with a random policy, recording total rewards, and comparing best and worst policies to analyze average performance.

Simulate the frozen lake environment by applying a 16-action policy over 16 states, illustrating movement, stochastic outcomes, and episode rewards, while examining the transformation matrix and rewards.

Apply value iteration to solve the frozen lake MDP and derive the optimal policy while learning convergence criteria and implementation steps.

continue with project by solving a frozen lake mdp using value iteration, setting a 0.99 discount factor and 0.0001 convergence threshold, and computing optimal values from states, actions, and rewards.

Solve an mdp with a value iteration algorithm, compute optimal values and the optimal policy, and evaluate performance with thousand episodes and average rewards.

Explore how value iteration solves an MDP by applying the Bellman optimality equation to obtain the optimal value function, and assess effects of discount and risk factors on total rewards.

Investigate solving an MDP with a value iteration algorithm by testing various discount factors and observing that higher gamma improves performance and reveals the optimal value.

Continuing a reinforcement learning project, the lecture applies value iteration to solve an MDP, extracts the optimal policy, and shows improving total rewards as the project concludes.

Explore solving an mdp with a policy iteration algorithm. Start from an arbitrary policy and iteratively perform policy evaluation and policy improvements until the policy stops changing.

Explore solving an mdp with a policy iteration algorithm, covering policy evaluation and policy improvements via Bellman equations, and apply it to the frozen lake environment using gym and torch.

Develop the second component of policy iteration by using environment inputs to improve the policy, building on policy evaluation toward an optimal policy.

Demonstrates solving an MDP with a policy iteration algorithm by plugging in the environment, tracing convergence to obtain optimal values and the optimal policy, and validating against value iteration.

Solve an mdp with policy iteration by evaluating a policy via the beelman expectation equation until convergence, then improve and repeat.

Compare policy iteration and value iteration for solving an MDP, highlighting faster convergence and when to use each, with a preview of applying them to a future problem.

Explore solving a coin-flipping gambling problem as an undiscounted finite mdp, modeling the gambler's capital with bets and a 40 percent win probability, and derive value and policy via iteration.

Implement a coin-flipping gamble problem using value iteration in a 101-state environment, computing rewards, transition dynamics, and an optimal policy with a 0.4 win probability.

Explore deriving the optimal policy and values for a coin-flipping gamble problem using value iteration, comparing it with policy iteration, and implementing the environment for convergence.

Explore value iteration and policy iteration techniques for the coin-flipping gamble problem part 3, deriving optimal policies and values.

Explore policy evaluation and policy iteration in the coin-flipping gamble environment, implementing value estimates, discount factor gamma, threshold stopping, and policy improvement to derive optimal actions.

Explore solving the coin-flipping gamble environment with policy iteration and value iteration, deriving optimal values and policies via the Bellman optimality equation, and compare efficiency with different action counts.

Explore solving the coin-flipping gamble problem by policy iteration, comparing it with value iteration, using the Belmont optimality equation to maximize winning probability and evaluate policy values.

Explore the coin-flipping gamble problem by comparing three strategies—optimal policy, conservative betting, and random betting—through thousands of episodes that track capital and winning probability.

Compare optimal, conservative, and random strategies in the coin-flipping gamble, implement episodes to evaluate capital-based outcomes, and measure the winning probabilities across policies.

Explore model-free reinforcement learning with Monte Carlo methods that require no prior environment knowledge, estimating value and action-value, and applying epsilon-greedy policy and weighted importance sampling on blackjack.

Estimate pi with the Monte Carlo method by generating random points in a square and counting those inside the circle of radius one to derive pi from area ratios.

Explore a Monte Carlo pi estimation by drawing random points in a square, counting those inside the unit circle, and computing pi as four times the inside-to-total ratio, with visualization.

Use Monte Carlo to estimate pi by random points inside a circle, illustrating convergence via the law of large numbers with increasing iterations.

learn how to estimate pi with the Monte Carlo method by randomly placing points in a square and counting those inside a circle, showing convergence to pi with more iterations.

Learn Monte Carlo policy evaluation for estimating value functions, comparing model-based and model-free approaches, and applying first-visit and every-visit predictions in the frozen lake environment.

Implement Monte Carlo policy evaluation in reinforcement learning by coding an episode-based framework that tracks rewards, states, and actions to estimate an optimal policy.

Explore Monte Carlo policy evaluation by implementing every-visit value predictions, updating value functions from episode returns, and applying model-free reinforcement learning concepts.

Learn Monte Carlo policy evaluation by estimating state values from episodic returns, averaging rewards over episodes, and evaluating policies without knowing the environment's transition or reward matrices.

Explore blackjack through Monte Carlo prediction and control, simulating episodes to evaluate a simple policy and move toward the optimal policy using Monte Carlo methods.

Explore blackjack with Monte Carlo prediction to evaluate policies and prepare for Monte Carlo control, using episodes, actions, and game rules to build optimal strategies.

Continue the blackjack Monte Carlo prediction project to evaluate a blackjack policy, estimate value with Monte Carlo methods, and simulate episodes, rewards, and environment dynamics.

Explore how Monte Carlo prediction estimates the blackjack value function from episodes, using first-visit returns and Monte Carlo updates to guide decision making.

Advance the blackjack project by implementing a Monte Carlo prediction-based value function, and build utility and plotting code to visualize state values across scenarios.

Apply Monte Carlo prediction to blackjack by defining states as player sum, dealer card, and usable ace; update value functions with first-visit returns and visualize them in a three-dimensional plot.

Learn on-policy Monte Carlo control by evaluating a policy's value via the Q function and applying first-visit updates to improve toward an optimal policy, with blackjack and 500000 episodes.

Implement on-policy Monte Carlo control by building a policy, sampling episodes in the environment, computing returns, and updating to obtain the optimal policy.

This lecture demonstrates implementing on-policy Monte Carlo control, updating q-values and value functions across episodes, evaluating and improving the policy, and visualizing results, including exploring start and first-visit updates.

This lecture demonstrates on-policy Monte Carlo control for blackjack, using a value function and 100,000 episodes to compare a simple policy with the optimal policy, highlighting improved win rates.

Explore on-policy Monte Carlo control by alternating evaluation and improvement across episodes, using exploring starts, a Q function, and first-visit evaluation to converge on an optimal policy.

Explore the epsilon-greedy policy for Monte Carlo control, balancing exploration and exploitation with the epsilon parameter to avoid local optima and improve action-value estimates.

This lecture demonstrates MC control with an epsilon-greedy policy, showing episode-based learning, environment interactions, Q updates, and the exploration–exploitation trade-off with epsilon scheduling.

Explore mc control with an epsilon-greedy policy over one hundred thousand episodes, print the winning policy, and compare the winning and losing probabilities to evaluate performance.

Explore Monte Carlo control with an epsilon-greedy policy balancing exploitation and exploration through action probabilities. The video compares epsilon schedules and shows win rates: 42.44% with epsilon-greedy vs 41.21% without.

Learn off-policy Monte Carlo control by using a behavior policy for exploration to improve a target policy through importance sampling, enabling policy evaluation and learning from episodes.

Learn to implement off-policy Monte Carlo control incrementally to update the Q function on the fly. The approach reduces memory usage and improves scalability.

Explore off-policy Monte Carlo control, using a behavior policy to generate episodes, evaluate a target policy with first-visit Monte Carlo, and incrementally update Q to obtain the optimal policy.

Monte Carlo control with weighted importance sampling, compare it to ordinary sampling, and reduce variance by using weighted returns from a behavior policy to optimize the target policy.

Implement mc control with weighted importance sampling, building a behavior policy, calculating q-values, and updating to approach the optimal policy in an episodic blackjack environment.

Explore how Monte Carlo control uses weighted importance sampling to scale returns and achieve lower variance than ordinary importance sampling.

The lecture explains weighted importance sampling for Monte Carlo control, showing variance reduction and a 100,000-episode simulation to estimate win and loss probabilities under the optimal policy.

Explore reinforcement learning from Monte Carlo methods to temporal-difference updates, learning action values without prior environment knowledge, and apply Q-learning and SARSA to the taxi problem.

Learn to set up the cliff walking environment playground, explore the gym-like observation and action spaces, and implement reset, render, and step workflows to test an agent.

Learn to implement the cliff walking environment with gym, starting at the bottom-left and reaching the bottom-right goal, with -1 rewards per step and -100 on cliffs.

Explore the cliff walking environment with a step-by-step playground walkthrough using a grid world, illustrating agent moves, resets, and rewards in reinforcement learning.

Introduce the q-learning algorithm and its temporal-difference update, derive the q-function update q(s,a) = q(s,a) + α [r + γ max_a' q(s',a') − q(s,a)], and apply epsilon-greedy policy.

Implement the Q-learning algorithm with an epsilon-greedy policy, updating the Q-function on each step to derive an optimal policy from simulated episodes.

This lecture covers implementing the q-learning algorithm, tracking total rewards per episode, and plotting progress over time, including episodic updates and the role of epsilon.

Implement q-learning by updating the q-function at every step with an epsilon-greedy policy, starting from a zero-initialized q-table and refining the optimal policy across episodes.

Explore the windy gridworld environment, where an external force moves the agent and complicates policy learning. Examine transition dynamics, negative one rewards, and the temperature difference method.

Implement a windy gridworld environment by setting up wind, building transition and reward matrices, computing the initial state distribution, and rendering the environment for visualization.

Explore the windy gridworld environment in reinforcement learning with part 2 of the implementation, experimenting with wind effects, reset and render, and understanding rewards of negative one.

Explore a windy gridworld environment and how wind adds an upward push, illustrating reinforcement learning dynamics, and note the upcoming temperature difference with red hot and ice.

The lecture introduces SARSA, an on-policy reinforcement learning method using an epsilon-greedy policy, updates q(s,a) with alpha and gamma, and outlines implementing it in the Windy Ritvo environment.

Implement the SARSA algorithm with epsilon-greedy action selection, updating Q-values at each step to learn the policy, and compare on-policy versus off-policy learning across episodes in the environment.

Continue the SARSA algorithm implementation by plotting total rewards per episode over time to verify the optimal policy and illustrate how reducing epsilon lowers fluctuations in exploration.

Explain the SARSA algorithm with on-policy, epsilon-greedy updates to q-values, using per-step updates and 500 episodes to converge toward the optimal policy.

Explore the taxi problem in a grid world using Q-learning. Episodes randomly assign taxi start, passenger, and destination; rewards for delivery and penalties for illegal actions.

Explore the taxi problem using q-learning, building and running a simulated environment, implementing epsilon-greedy policy, updating q-values, and evaluating rewards across episodes.

Explore how the taxi problem is solved with Q-learning, showing rewards, episode counts, and convergence behavior using an epsilon-greedy policy to update values from experience.

Learn to solve the taxi problem with SARSA by defining the behavior policy, running episodes, and tracking total rewards while tuning epsilon and other parameters.

Implement SARSA on the taxi problem, tune the epsilon-greedy policy and learning rate to maximize episode rewards and converge to an optimal Q-function.

Explore the taxi problem with SARSA implementation in reinforcement learning, tuning alpha and epsilon, and evaluating average rewards across episodes to identify the best parameter settings.

explores solving the taxi problem with sarsa, using epsilon-greedy updates and live plotting of episode rewards to verify convergence and compare hyperparameters for best performance.

Explore the double Q-learning algorithm to reduce overestimation by using two Q-functions, Q1 and Q2, and updating one at a time to obtain the optimal policy.

Implement double q-learning for the taxi problem, building the environment, q functions, and epsilon-greedy policy to learn an optimal policy through episodes and rewards.

Explore double q-learning by updating one of two q-functions chosen at random to reduce overestimation. Observe how averaging the two q-functions guides policy convergence across episodes.

Explore reinforcement learning fundamentals, including dynamic programming, Monte Carlo methods, and a temperature difference method, and review exploration strategies such as epsilon and confidence-based approaches.

Explores the multi-armed bandit problem with unknown reward distributions, and introduces exploration strategies and epsilon-based exploration to balance exploration and exploitation.

Create a bandit environment with the gym toolkit, integrate it with the game, and download and mount drive in Colab to test arm probabilities.

Apply the epsilon-greedy bandit approach to a two-armed setup, balance exploration and exploitation, and update arm estimates to identify the best arm with the highest average reward.

Explore bandit strategies in gym part 3 by comparing epsilon-greedy and softmax exploration, using temperature to bias arm selection by average reward during initial exploration rounds.

Explore the exploration-exploitation dilemma in reinforcement learning bandits with upper confidence bound methods, learning to select arms by confidence intervals and updating rewards to maximize long-term performance.

Explore Hobson sampling, a probabilistic bandit method using priors to balance exploration and exploitation by sampling from arm distributions, pulling the best arm, and updating alpha and delta after outcomes.

Learn how to create a bandit in the Gym, using Thompson sampling, updating alpha and related values, sampling rewards, and identifying the optimal outcome from observed rewards.

Explore how multi-armed bandit methods apply to real-world use cases beyond A/B testing, optimizing landing pages and online ads by balancing exploration and exploitation to maximize conversions.

Apply a multi-armed bandit approach to select the best advertisement banner by using an epsilon-greedy exploration strategy, maximizing the average reward across five ad panels.

Explore reinforcement learning strategies with the epsilon-greedy method and probability-based exploration, selecting random actions with epsilon and the best action otherwise, while weighting by average reward to learn value estimates.

Explore solving internet advertising problems with contextual bandits by incorporating state descriptions, actions (arms), and context such as demographics, applying the UCB policy to optimize rewards.

Explore solving internet advertising problems with contextual bandits by introducing state to guide actions, rewards, and payouts, and implement a UCB policy across multiple arms.

Learn to solve internet advertising problems with contextual bandits, focusing on two states, actions, and rewards. Compare contextual bandits with broader reinforcement learning and explore practical ad experiments.

Explain how contextual bandits solve internet advertising problems using ucb algorithms, track the average reward over time, and emphasize state-based actions without delay.

Introduce deep q networks as modern reinforcement learning techniques, and build vanilla q networks step by step, then improve with a target network and compare their value functions across networks.

Explore deep Q-networks that map states to action values with neural networks. Train with Q-learning and an epsilon-greedy policy, track rewards over 1000 episodes.

Explore a deep q-networks implementation, set up the environment, and run a training loop that updates a neural network with q-values and target values using epsilon-greedy exploration.

continue implementing deep q-networks by using epsilon-greedy actions, updating q-values with rewards, and evaluating episode performance in part 2.

Implement deep q-networks with an epsilon-greedy policy, train a neural network to estimate action values, and update targets via backpropagation across episodes with reward signals.

introduce the double deep q-network, using a separate target network to estimate targets while a prediction network selects actions, updating target weights after each episode to stabilize training.

This lecture walks through implementing double deep Q-networks, setting up the environment, building the replay memory and target network, and configuring epsilon-based training with periodic target updates.

Double deep q-networks use separate prediction and target networks, convert experiences into training samples, and synchronize periodically to stabilize learning and outperform a single dqn.

Learn how dueling deep q-networks decouple the value and advantage functions to compute q-values, enabling efficient deep q-learning with an epsilon-greedy policy in a car environment.

Implement dueling deep q-networks by building the environment and a neural network with experience memory. Learn training with rewards per episode and an epsilon-based policy using q-values and advantages.

Explore the dueling deep q-nets architecture, which combines a value estimate with an action advantage to improve learning robustness in reinforcement learning.

Introduce reinforcement learning concepts, implement the reinforce algorithm with a baseline, and explore an actor with variations applied to shoulder cable and clip walking problems.

Learn how the reinforce algorithm uses Monte Carlo trajectories to optimize the action probability distribution, rewarding better actions and updating the policy after each episode via backpropagation.

This lecture explains the reinforce algorithm implementation in reinforcement learning with a neural network policy, action probabilities, episode-based updates, and training from rewards using gamma-based returns.

Learn to implement the REINFORCE algorithm with a policy network, collect episodes, compute returns, normalize them, and update policy parameters via backpropagation.

Explore the REINFORCE algorithm with a baseline to reduce high variance from stochastic policies, apply the advantage function in gradient updates, and estimate values with linear function or neural network.

Demonstrates implementing reinforce with a baseline by building a policy network and a value network, computing advantages, and updating network weights via backpropagation in an environment.

Explore reinforce with baseline method, training over 2000 episodes to reduce gradient variance with a baseline value, updating policy and value networks based on advantage estimates.

Implement the actor-critic approach by building a two-part neural network. An actor outputs action probabilities, and a critic estimates value to guide policy updates.

Implement the actor-critic algorithm by learning an actor that outputs action probabilities and a critic that estimates state values, using advantage and negative log-likelihood to reduce variance.

Explore cliff walking in a gym environment using the actor-critic algorithm, with long episodes and -100 cliff penalties, and learn one-hot state encoding for the network.

Explore cliff walking using the actor-critic algorithm, implementing a policy network and a critic, training episodes with rewards to learn optimal navigation.

Learn cliff walking with the actor-critic algorithm in part 2, implementing a more stable policy with a neural network with two hidden layers encoding 148 input dimensions.

Set up the mountain car environment with continuous actions, explain momentum and continuous action values, and explore the reward, velocity, and goal-reaching dynamics.

Explore solving the mountain car environment with an advantage actor-critic method for continuous actions, using a Gaussian action distribution, a policy network, and value estimation.

Learn to solve the mountain car environment using reinforcement learning by building a modular model with an actor and critic components and training a policy network to maximize rewards.

Solve the mountain car continuous control task with a Gaussian policy network that outputs mean, variance, and value, and train with actor-critic loss and clipped actions.

Advance your reinforcement learning skills through persistent practice, set your own goals and challenges, and build models from datasets for ongoing learning in future courses.