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TF Depend GYM Depend License Badge

Deep Reinforcement Learning in TensorFlow2

DeepRL-TensorFlow2 is a repository that implements a variety of popular Deep Reinforcement Learning algorithms using TensorFlow2. The key to this repository is an easy-to-understand code. Therefore, if you are a student or a researcher studying Deep Reinforcement Learning, I think it would be the best choice to study with this repository. One algorithm relies only on one python script file. So you don't have to go in and out of different files to study specific algorithms. This repository is constantly being updated and will continue to add a new Deep Reinforcement Learning algorithm.

Algorithms

DQN

Paper Playing Atari with Deep Reinforcement Learning
Author Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, Martin Riedmiller
Method OFF-Policy / Temporal-Diffrence / Model-Free
Action Discrete only

Core of Idea

# idea01. Approximate Q-Function using NeuralNetwork
def create_model(self):
    model = tf.keras.Sequential([
        Input((self.state_dim,)),
        Dense(32, activation='relu'),
        Dense(16, activation='relu'),
        Dense(self.action_dim)
    ])
    model.compile(loss='mse', optimizer=Adam(args.lr))
    return model

# idea02. Use target network
self.target_model = ActionStateModel(self.state_dim, self.action_dim)
 
# idea03. Use ReplayBuffer to increase data efficiency
class ReplayBuffer:
    def __init__(self, capacity=10000):
        self.buffer = deque(maxlen=capacity)
    
    def put(self, state, action, reward, next_state, done):
        self.buffer.append([state, action, reward, next_state, done])
    
    def sample(self):
        sample = random.sample(self.buffer, args.batch_size)
        states, actions, rewards, next_states, done = map(np.asarray, zip(*sample))
        states = np.array(states).reshape(args.batch_size, -1)
        next_states = np.array(next_states).reshape(args.batch_size, -1)
        return states, actions, rewards, next_states, done
    
    def size(self):
        return len(self.buffer)

Getting Start

# Discrete Action Space Deep Q-Learning
$ python DQN/DQN_Discrete.py

DRQN

Paper Deep Recurrent Q-Learning for Partially Observable MDPs
Author Matthew Hausknecht, Peter Stone
Method OFF-Policy / Temporal-Diffrence / Model-Free
Action Discrete only

Core of Ideas

# idea01. Previous state uses LSTM layer as feature
def create_model(self):
    return tf.keras.Sequential([
        Input((args.time_steps, self.state_dim)),
        LSTM(32, activation='tanh'),
        Dense(16, activation='relu'),
        Dense(self.action_dim)
    ])

Getting Start

# Discrete Action Space Deep Recurrent Q-Learning
$ python DRQN/DRQN_Discrete.py

DoubleDQN

Paper Deep Reinforcement Learning with Double Q-learning
Author Hado van Hasselt, Arthur Guez, David Silver
Method OFF-Policy / Temporal-Diffrence / Model-Free
Action Discrete only

Core of Ideas

# idea01. Resolved the issue of 'overestimate' in Q Learning
on_action = np.argmax(self.model.predict(next_states), axis=1)
next_q_values = self.target_model.predict(next_states)[range(args.batch_size), on_action]
targets[range(args.batch_size), actions] = rewards + (1-done) * next_q_values * args.gamma

Getting Start

# Discrete Action Space Double Deep Q-Learning
$ python DoubleQN/DoubleDQN_Discrete.py

DuelingDQN

Paper Dueling Network Architectures for Deep Reinforcement Learning
Author Ziyu Wang, Tom Schaul, Matteo Hessel, Hado van Hasselt, Marc Lanctot, Nando de Freitas
Method OFF-Policy / Temporal-Diffrence / Model-Free
Action Discrete only

Core of Ideas

# idea01. Q-Function has been separated into Value Function and Advantage Function
def create_model(self):
    backbone = tf.keras.Sequential([
        Input((self.state_dim,)),
        Dense(32, activation='relu'),
        Dense(16, activation='relu')
    ])
    state_input = Input((self.state_dim,))
    backbone_1 = Dense(32, activation='relu')(state_input)
    backbone_2 = Dense(16, activation='relu')(backbone_1)
    value_output = Dense(1)(backbone_2)
    advantage_output = Dense(self.action_dim)(backbone_2)
    output = Add()([value_output, advantage_output])
    model = tf.keras.Model(state_input, output)
    model.compile(loss='mse', optimizer=Adam(args.lr))
    return model

Gettting Start

# Discrete Action Space Dueling Deep Q-Learning
$ python DuelingDQN/DuelingDQN_Discrete.py

A2C

Paper Actor-Critic Algorithms
Author Vijay R. Konda, John N. Tsitsiklis
Method ON-Policy / Temporal-Diffrence / Model-Free
Action Discrete, Continuous

Core of Ideas

# idea01. Use Advantage to reduce Variance
def advatnage(self, td_targets, baselines):
    return td_targets - baselines

Getting Start

# Discrete Action Space Advantage Actor-Critic
$ python A2C/A2C_Discrete.py

# Continuous Action Space Advantage Actor-Critic
$ python A2C/A2C_Continuous.py

A3C

Paper Asynchronous Methods for Deep Reinforcement Learning
Author Volodymyr Mnih, Adrià Puigdomènech Badia, Mehdi Mirza, Alex Graves, Timothy P. Lillicrap, Tim Harley, David Silver, Koray Kavukcuoglu
Method ON-Policy / Temporal-Diffrence / Model-Free
Action Discrete, Continuous

Core of Ideas

# idea01. Reduce the correlation of data by running asynchronously multiple workers
def train(self, max_episodes=1000):
    workers = []

    for i in range(self.num_workers):
        env = gym.make(self.env_name)
        workers.append(WorkerAgent(
            env, self.global_actor, self.global_critic, max_episodes))

    for worker in workers:
        worker.start()

    for worker in workers:
        worker.join()

# idea02. Improves exploration through entropy loss
entropy_loss = tf.keras.losses.CategoricalCrossentropy(from_logits=True)

Getting Start

# Discrete Action Space Asyncronous Advantage Actor-Critic
$ python A3C/A3C_Discrete.py

# Continuous Action Space Asyncronous Advantage Actor-Critic
$ python A3C/A3C_Continuous.py

PPO

Paper Proximal Policy Optimization
Author John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, Oleg Klimov
Method ON-Policy / Temporal-Diffrence / Model-Free
Action Discrete, Continuous

Core of ideas

# idea01. Use Importance Sampling to act like an Off-Policy algorithm
# idea02. Use clip to prevent rapid changes in parameters.
def compute_loss(self, old_policy, new_policy, actions, gaes):
    gaes = tf.stop_gradient(gaes)
    old_log_p = tf.math.log(
        tf.reduce_sum(old_policy * actions))
    old_log_p = tf.stop_gradient(old_log_p)
    log_p = tf.math.log(tf.reduce_sum(
        new_policy * actions))
    ratio = tf.math.exp(log_p - old_log_p)
    clipped_ratio = tf.clip_by_value(
        ratio, 1 - args.clip_ratio, 1 + args.clip_ratio)
    surrogate = -tf.minimum(ratio * gaes, clipped_ratio * gaes)
    return tf.reduce_mean(surrogate)

Getting Start

# Discrete Action Space Proximal Policy Optimization
$ python PPO/PPO_Discrete.py

# Continuous Action Space Proximal Policy Optimization
$ python PPO/PPO_Continuous.py

DDPG

Paper Continuous control with deep reinforcement learning
Author Timothy P. Lillicrap, Jonathan J. Hunt, Alexander Pritzel, Nicolas Heess, Tom Erez, Yuval Tassa, David Silver, Daan Wierstra
Method OFF-Policy / Temporal-Diffrence / Model-Free
Action Continuous

Core of ideas

# idea01. Use deterministic Actor Model
def create_model(self):
    return tf.keras.Sequential([
        Input((self.state_dim,)),
        Dense(32, activation='relu'),
        Dense(32, activation='relu'),
        Dense(self.action_dim, activation='tanh'),
        Lambda(lambda x: x * self.action_bound)
    ])

# idea02. Add noise to Action
action = np.clip(action + noise, -self.action_bound, self.action_bound)

Getting Start

# Continuous Action Space Proximal Policy Optimization
$ python DDPG/DDPG_Continuous.py

TRPO

Paper Trust Region Policy Optimization
Author John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, Pieter Abbeel
Method OFF-Policy / Temporal-Diffrence / Model-Free
Action Discrete, Continuous

# NOTE: Not yet implemented!

TD3

Paper Addressing Function Approximation Error in Actor-Critic Methods
Author Scott Fujimoto, Herke van Hoof, David Meger
Method OFF-Policy / Temporal-Diffrence / Model-Free
Action Continuous

# NOTE: Not yet implemented!

SAC

Paper Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
Author Tuomas Haarnoja, Aurick Zhou, Pieter Abbeel, Sergey Levine
Method OFF-Policy / Temporal-Diffrence / Model-Free
Action Discrete, Continuous

# NOTE: Not yet implemented!

Reference