README.md

Deep Reinforcement Learning in Keras and ViZDoom

Implementation of deep reinforcement learning algorithm on the Doom environment

The features that were implemented are:

• DQN
• Double DQN
• Prioritized Experience Replay
• Next state prediction using autoencoder + GAN (WIP)
• Next state prediction using VAE (WIP)
• Exploration policies: e-greedy, softmax or shifted multinomial
• Architectures: Sequential Q estimation, Inception Q estimation, Dueling Q estimation
• Macro-actions prediction using LSTM and n-step Q learning

trained models are also supplied

Results

DDQN runs:

State prediction:

actual:

predicted:

actual:

predicted:

Exploration policies:

Tested on health gathering level for 1000 episodes

red - softmax, green - shifted multinomial, blue - e-greedy

Details

DQN

Deep Q-Network implementation

Reference: https://www.cs.toronto.edu/~vmnih/docs/dqn.pdf

DDQN

Double Deep Q-Network implementation

Details: Reduces value overestimation in DQN

Reference: https://arxiv.org/pdf/1509.06461.pdf

Prioritized Experience Replay

Chooses the most influencing states from the experience replay by using the TD-error as the priority

Reference: http://www0.cs.ucl.ac.uk/staff/d.silver/web/Publications_files/prioritized-replay.pdf

Next state prediction

Action-conditional video prediction implementation

Details: Predicts the next state given the current state and an action to simulate the value function of actions not actually taken uses an Autoencoder integrated into a Generative Adverserial Network

Partial reference: https://sites.google.com/a/umich.edu/junhyuk-oh/action-conditional-video-prediction

Exploration policies

e-Greedy - Choose an epsilon and choose a random number. If the number is greater than epsilon, choose the max value action. Otherwise, choose a random action.

Softmax - Choose a random number and select the action by a multinomial probability ordered by prob(a) = e^(Q(a)/temp)/sum(e^(Q(a)/temp)).

Shifted Multinomial - Similiar to softmax but chooses the action by the order shifted_Q(a) = Q(a)-min(avg(min(Q(a)), min(Q(a))). prob(a) = shifted_Q(a)/sum(shifted_Q(a)).

Dueling Network Architecture

Estimates the state-value function V and the action advantage function A and combines them to produce the state-action value Q as part of the deep network.

Reference: https://arxiv.org/pdf/1511.06581.pdf

More Results

Basic Level DQN training process

Average return over 10000 episodes

Basic Level DDQN training process

Average return over 10000 episodes

Health Gathering Level DDQN training process

Average return over 500 episodes

Author

Itai Caspi