Artificial Intelligence IV - Reinforcement Learning in Java

Examine the three main types of learning: supervised, unsupervised, and reinforcement learning. See how labels, datasets, clustering, decision boundaries, and rewards guide models on xor and tic tac toe.

Explore reinforcement learning applications in games like Mario, chess, and Go, plus driving, flying, finance, and logistics, highlighted by deep neural networks and Q-learning.

Define Markov decision processes with states, actions, transition probabilities, rewards, and a discount factor, and explain the Markov property underpinning reinforcement learning algorithms such as value and policy iteration.

Introduce Markov decision processes for reinforcement learning by defining states, actions, a transition probability matrix, and gamma. Discuss the Markov property and deterministic versus stochastic environments, plus the discount factor.

Defines the key Markov decision process equations, including terminal states, discounted rewards with gamma, and the relationships among policy, value, and Q functions to identify the optimal policy.

Explore Markov decision processes, including the value function, the policy, and the Q functions, and see how gamma discounting and a stochastic environment shape optimal actions and cumulative rewards.

Apply the Bellman equation to Markov decision processes, defining value, policy, and Q functions to maximize rewards using immediate and discounted future rewards and transition probabilities.

Explore how the Bauman equation guides solving Markov decision processes, comparing offline model-based value iteration and policy iteration with online model-free Q-learning, and implement value iteration before emphasizing Q-learning.

Value iteration builds the optimal value function V* by iteratively updating V using the Baumann equation, handling stochastic transitions and unknown models to converge across all states.

Policy iteration updates the policy function to convergence on a Markov decision process and compares to value iteration, highlighting Q-learning as a popular reinforcement learning alternative.

Compare value iteration and policy iteration as offline model-based Markov decision process solvers. Q-learning offers a model-free, online alternative that learns from experience and pairs with deep neural networks.

Investigate the exploration versus exploitation trade-off in reinforcement learning, contrasting model-based value methods with model-free Q-learning, and introduce the multi-armed bandits problem as the first step.

Explore how the multi-armed bandit problem trains an agent to approximate the success rates of three bandits using epsilon-greedy exploration and exploitation with Q-value updates.

Implement the n-armed bandit in java with an epsilon-greedy agent that balances exploration and exploitation, updating q-values after rewards for three bandits with 70, 30, and 50 percent success rates.

Explore how the epsilon-greedy approach handles the multi-armed bandit problem and the role of iterations in exploration. Observe convergence toward the optimal bandit as iterations grow, balancing exploration and exploitation.

Apply epsilon-greedy reinforcement learning to online marketing AB tests, balancing exploration and exploitation to maximize conversions by selecting ads, logos, and call-to-actions while facing unknown user preferences.

Explore Q-learning, a model-free, online reinforcement learning method where an agent learns optimal actions by trial and error, updating Q values via time-difference learning to maximize discounted rewards.

Introduce Q learning by initializing gamma, alpha, reward matrix, and Q values, then run episodes to update the Q matrix using the maximum of next-state action values for policy.

Q learning illustration demonstrates updating Q values for every state in a grid with +1 terminal reward and -1 penalty, using random actions and the Bamyan equation.

Learn to implement a Q-learning algorithm from scratch using a Q-table to navigate a 4x4 maze, updating the policy after each move through episodes and iterations.

Implement a q-learning maze environment using a 2d int array, track agent and goal positions, flatten the state to 1d, and include reset and move methods with four actions.

Learn how q-learning maps actions to maze moves with up, down, left, right, enforces boundaries and avoids walls, and rewards reaching the goal with +10 while penalizing steps with -1.

Implement a q-learning agent by defining a maze environment, a 16x4 q-table for states and actions (up, down, left, right), and parameters such as learning rate, gamma, and epsilon.

Train a reinforcement learning agent in a maze using q-learning with an epsilon-greedy policy over many episodes, updating the q-table via the Bellman equation and state flattening.

Visualize the q-learning agent's policy on a grid by printing the best action for every state, including obstacles, one dimensional state index, and symbols for up, down, left, and right.

Evaluate a Q-learning implementation by running episodes to reveal improvements in the q-table and the agent's optimal policy in a gridworld maze, and discuss upcoming deep Q-learning with neural networks.

Explore deep Q-learning as a scalable alternative to tabular Q-learning, using a convolutional neural network to approximate Q values from game frames and select actions.

Explore the exploration-exploitation trade-off in deep Q-learning with the epsilon-greedy strategy, starting from a high epsilon and decaying to favor exploitation while updating the neural network.

Explore how deep q-learning uses an experience replay buffer to store transitions, sample randomly to break temporal correlation, with buffers like 100,000 to 10 million, and 10,000 for non-stationary tasks.

Employ a value network and a separate target network to stabilize training, utilize a replay buffer to break correlations, and apply target values derived from the Bellman equation.

Implement deep q-learning in a maze where the agent starts at the top left and reaches the bottom right terminal, using a single integer state and four actions.

Implement a deep q-network for reinforcement learning using deep learning for j, with 16-input, 128- and 64-hidden, 4-output layer, mean squared error loss, and Adam optimizer.

Predict q-values with a neural network, batch multiple states and actions for training with fit, and use a target network to decouple targets from predictions in deep q-learning.

Implement a replay buffer with a transition class to store state, action, reward, next state, and done, enabling random sampling for stable, diverse training.

Implement a deep Q-learning agent in Java by constructing a Q network, replay buffer, and learning loop, using epsilon-greedy strategy and one-hot encoded 4x4 states to choose actions via argmax.

train the neural network using a replay buffer with batch-sized samples, applying epsilon-greedy action selection to learn from past experiences and update after each episode.

Implement deep q-learning by sampling batches from the replay buffer, using one-hot encoded states, and computing targets from rewards and the next state's max q-value, updating only the taken action.

Print the optimal policy for each cell in a two-dimensional maze after training a deep Q-learning agent, mapping actions to up, down, left, and right symbols.

Explore why deep q-learning, based on a neural q-function and greedy policy for discrete actions, suffers instability from correlated data and catastrophic forgetting, with difficulties in continuous spaces.

Explore trust region policy optimization (trpo) as a model-free policy gradient approach, featuring an actor-critic architecture, kl divergence constraints, and batched updates for stability over deep q-learning.

Explore the trust region policy optimization (trpo) approach in reinforcement learning, using actor-critic architecture with stochastic policies and batched trajectories to ensure stable, gradual policy updates via kl divergence.

Explore proximal policy optimization (ppo), an actor-critic method with separate policy and value networks, and see how the advantage function guides policy updates via td or gae.

Explains how to approximate the advantage function with temporal-difference methods and generalized advantage estimation, using a neural value function, discounted rewards, gamma, and lambda.

Learn how the value function loss uses regression against the discounted cumulative reward, optimized by gradient descent over multiple trajectories, to improve critic and policy networks via the advantage function.

The lecture explains the policy loss in proximal policy optimization, using the policy ratio of the new and old policies multiplied by the advantage, with clipping to keep updates small.