From 2ad8b9f4d20c1f680f30f0627300ee65167291cd Mon Sep 17 00:00:00 2001 From: chenyingyinglalala <18759377116@163.com> Date: Sun, 3 Nov 2019 10:39:13 +0800 Subject: [PATCH] add --- Char00 Conventional Algorithms/Q-learning.py | 98 ++++++ Char00 Conventional Algorithms/Sarsa.py | 104 ++++++ Char00 Conventional Algorithms/gridworld.py | 152 ++++++++ Char01 DQN/DQN.py | 143 ++++++++ Char01 DQN/DQN/pic/finish_episode.jpg | Bin 0 -> 35678 bytes Char01 DQN/DQN/pic/readme.md | 1 + Char01 DQN/DQN/pic/value_loss.jpg | Bin 0 -> 35850 bytes Char01 DQN/DQN_CartPole-v0.py | 120 +++++++ Char01 DQN/DQN_MountainCar-v0.py | 120 +++++++ Char01 DQN/DQN_mountain_car_v1.py | 133 +++++++ Char01 DQN/naiveDQN.py | 124 +++++++ Char01 DQN/readme.md | 14 + Char02 Policy Gradient/PolicyGradient.py | 106 ++++++ Char02 Policy Gradient/REINFORCE.py | 103 ++++++ .../REINFORCE_with_Baseline.py | 154 +++++++++ Char02 Policy Gradient/Run_Model.py | 104 ++++++ .../naive-policy-gradient.py | 153 +++++++++ .../pytorch_MountainCar-v0.py | 134 ++++++++ Char03 Actor-Critic/AC_CartPole-v0.py | 128 +++++++ Char03 Actor-Critic/AC_MountainCar-v0.py | 145 ++++++++ Char04 A2C/A2C.py | 158 +++++++++ Char04 A2C/multiprocessing_env.py | 153 +++++++++ Char05 DDPG/DDPG.py | 268 +++++++++++++++ Char05 DDPG/DDPG_exp.jpg | Bin 0 -> 62682 bytes Char05 DDPG/README.md | 10 + Char07 PPO/PPO2.py | 188 ++++++++++ Char07 PPO/PPO_CartPole_v0.py | 174 ++++++++++ Char07 PPO/PPO_MountainCar-v0.py | 176 ++++++++++ Char07 PPO/PPO_pendulum.py | 195 +++++++++++ Char07 PPO/readme.md | 6 + Char08 ACER/readme.md | 3 + Char09 SAC/SAC.py | 307 +++++++++++++++++ Char09 SAC/SAC_BipedalWalker-v2.py | 321 +++++++++++++++++ Char09 SAC/SAC_dual_Q_net.py | 312 +++++++++++++++++ Char09 SAC/SAC_ep_r_curve.png | Bin 0 -> 63653 bytes Char09 SAC/test_agent.py | 324 ++++++++++++++++++ .../Episode_reward_TD3_BipedakWalker.png | Bin 0 -> 91372 bytes Char10 TD3/TD3.py | 309 +++++++++++++++++ Char10 TD3/TD3_BipedalWalker-v2.py | 313 +++++++++++++++++ Char10 TD3/TD3_Pendulum-v0.png | Bin 0 -> 48669 bytes Char10 TD3/expTD3.pyPendulum-v0./actor.pth | Bin 0 -> 489901 bytes .../expTD3.pyPendulum-v0./actor_target.pth | Bin 0 -> 489901 bytes Char10 TD3/expTD3.pyPendulum-v0./critic_1.pth | Bin 0 -> 491501 bytes .../expTD3.pyPendulum-v0./critic_1_target.pth | Bin 0 -> 491501 bytes Char10 TD3/expTD3.pyPendulum-v0./critic_2.pth | Bin 0 -> 491501 bytes .../expTD3.pyPendulum-v0./critic_2_target.pth | Bin 0 -> 491501 bytes .../actor.pth | Bin 0 -> 527113 bytes .../actor_target.pth | Bin 0 -> 527113 bytes .../critic_1.pth | Bin 0 -> 529901 bytes .../critic_1_target.pth | Bin 0 -> 529901 bytes .../critic_2.pth | Bin 0 -> 529901 bytes .../critic_2_target.pth | Bin 0 -> 529901 bytes LICENSE | 21 ++ More/Application in real world/README.md | 6 + More/MARL/README.md | 16 + More/plot.py | 51 +++ More/readme.md | 3 + figures/test.png | Bin 0 -> 11651 bytes readme.md | 203 +++++++++++ requirements.txt | 6 + 60 files changed, 5559 insertions(+) create mode 100644 Char00 Conventional Algorithms/Q-learning.py create mode 100644 Char00 Conventional Algorithms/Sarsa.py create mode 100644 Char00 Conventional Algorithms/gridworld.py create mode 100644 Char01 DQN/DQN.py create mode 100644 Char01 DQN/DQN/pic/finish_episode.jpg create mode 100644 Char01 DQN/DQN/pic/readme.md create mode 100644 Char01 DQN/DQN/pic/value_loss.jpg create mode 100644 Char01 DQN/DQN_CartPole-v0.py create mode 100644 Char01 DQN/DQN_MountainCar-v0.py create mode 100644 Char01 DQN/DQN_mountain_car_v1.py create mode 100644 Char01 DQN/naiveDQN.py create mode 100644 Char01 DQN/readme.md create mode 100644 Char02 Policy Gradient/PolicyGradient.py create mode 100644 Char02 Policy Gradient/REINFORCE.py create mode 100644 Char02 Policy Gradient/REINFORCE_with_Baseline.py create mode 100644 Char02 Policy Gradient/Run_Model.py create mode 100644 Char02 Policy Gradient/naive-policy-gradient.py create mode 100644 Char02 Policy Gradient/pytorch_MountainCar-v0.py create mode 100644 Char03 Actor-Critic/AC_CartPole-v0.py create mode 100644 Char03 Actor-Critic/AC_MountainCar-v0.py create mode 100644 Char04 A2C/A2C.py create mode 100644 Char04 A2C/multiprocessing_env.py create mode 100644 Char05 DDPG/DDPG.py create mode 100644 Char05 DDPG/DDPG_exp.jpg create mode 100644 Char05 DDPG/README.md create mode 100644 Char07 PPO/PPO2.py create mode 100644 Char07 PPO/PPO_CartPole_v0.py create mode 100644 Char07 PPO/PPO_MountainCar-v0.py create mode 100644 Char07 PPO/PPO_pendulum.py create mode 100644 Char07 PPO/readme.md create mode 100644 Char08 ACER/readme.md create mode 100644 Char09 SAC/SAC.py create mode 100644 Char09 SAC/SAC_BipedalWalker-v2.py create mode 100644 Char09 SAC/SAC_dual_Q_net.py create mode 100644 Char09 SAC/SAC_ep_r_curve.png create mode 100644 Char09 SAC/test_agent.py create mode 100644 Char10 TD3/Episode_reward_TD3_BipedakWalker.png create mode 100644 Char10 TD3/TD3.py create mode 100644 Char10 TD3/TD3_BipedalWalker-v2.py create mode 100644 Char10 TD3/TD3_Pendulum-v0.png create mode 100644 Char10 TD3/expTD3.pyPendulum-v0./actor.pth create mode 100644 Char10 TD3/expTD3.pyPendulum-v0./actor_target.pth create mode 100644 Char10 TD3/expTD3.pyPendulum-v0./critic_1.pth create mode 100644 Char10 TD3/expTD3.pyPendulum-v0./critic_1_target.pth create mode 100644 Char10 TD3/expTD3.pyPendulum-v0./critic_2.pth create mode 100644 Char10 TD3/expTD3.pyPendulum-v0./critic_2_target.pth create mode 100644 Char10 TD3/expTD3_BipedalWalker-v2.pyBipedalWalker-v2./actor.pth create mode 100644 Char10 TD3/expTD3_BipedalWalker-v2.pyBipedalWalker-v2./actor_target.pth create mode 100644 Char10 TD3/expTD3_BipedalWalker-v2.pyBipedalWalker-v2./critic_1.pth create mode 100644 Char10 TD3/expTD3_BipedalWalker-v2.pyBipedalWalker-v2./critic_1_target.pth create mode 100644 Char10 TD3/expTD3_BipedalWalker-v2.pyBipedalWalker-v2./critic_2.pth create mode 100644 Char10 TD3/expTD3_BipedalWalker-v2.pyBipedalWalker-v2./critic_2_target.pth create mode 100644 LICENSE create mode 100644 More/Application in real world/README.md create mode 100644 More/MARL/README.md create mode 100644 More/plot.py create mode 100644 More/readme.md create mode 100644 figures/test.png create mode 100644 readme.md create mode 100644 requirements.txt diff --git a/Char00 Conventional Algorithms/Q-learning.py b/Char00 Conventional Algorithms/Q-learning.py new file mode 100644 index 0000000..14047b2 --- /dev/null +++ b/Char00 Conventional Algorithms/Q-learning.py @@ -0,0 +1,98 @@ +import numpy as np +import pandas as pd +import matplotlib.pyplot as plt +import time + +ALPHA = 0.1 +GAMMA = 0.95 +EPSILION = 0.9 +N_STATE = 20 +ACTIONS = ['left', 'right'] +MAX_EPISODES = 200 +FRESH_TIME = 0.1 + +def build_q_table(n_state, actions): + q_table = pd.DataFrame( + np.zeros((n_state, len(actions))), + np.arange(n_state), + actions + ) + return q_table + +def choose_action(state, q_table): + #epslion - greedy policy + state_action = q_table.loc[state,:] + if np.random.uniform()>EPSILION or (state_action==0).all(): + action_name = np.random.choice(ACTIONS) + else: + action_name = state_action.idxmax() + return action_name + +def get_env_feedback(state, action): + if action=='right': + if state == N_STATE-2: + next_state = 'terminal' + reward = 1 + else: + next_state = state+1 + reward = -0.5 + else: + if state == 0: + next_state = 0 + + else: + next_state = state-1 + reward = -0.5 + return next_state, reward + +def update_env(state,episode, step_counter): + env = ['-'] *(N_STATE-1)+['T'] + if state =='terminal': + print("Episode {}, the total step is {}".format(episode+1, step_counter)) + final_env = ['-'] *(N_STATE-1)+['T'] + return True, step_counter + else: + env[state]='*' + env = ''.join(env) + print(env) + time.sleep(FRESH_TIME) + return False, step_counter + + +def q_learning(): + q_table = build_q_table(N_STATE, ACTIONS) + step_counter_times = [] + for episode in range(MAX_EPISODES): + state = 0 + is_terminal = False + step_counter = 0 + update_env(state, episode, step_counter) + while not is_terminal: + action = choose_action(state,q_table) + next_state, reward = get_env_feedback(state, action) + next_q = q_table.loc[state, action] + if next_state == 'terminal': + is_terminal = True + q_target = reward + else: + delta = reward + GAMMA*q_table.iloc[next_state,:].max()-q_table.loc[state, action] + q_table.loc[state, action] += ALPHA*delta + state = next_state + is_terminal,steps = update_env(state, episode, step_counter+1) + step_counter+=1 + if is_terminal: + step_counter_times.append(steps) + + return q_table, step_counter_times + +def main(): + q_table, step_counter_times= q_learning() + print("Q table\n{}\n".format(q_table)) + print('end') + + plt.plot(step_counter_times,'g-') + plt.ylabel("steps") + plt.show() + print("The step_counter_times is {}".format(step_counter_times)) + +main() diff --git a/Char00 Conventional Algorithms/Sarsa.py b/Char00 Conventional Algorithms/Sarsa.py new file mode 100644 index 0000000..b96dbf7 --- /dev/null +++ b/Char00 Conventional Algorithms/Sarsa.py @@ -0,0 +1,104 @@ + +import numpy as np +import pandas as pd +import matplotlib.pyplot as plt +import time + +ALPHA = 0.1 +GAMMA = 0.95 +EPSILION = 0.9 +N_STATE = 6 +ACTIONS = ['left', 'right'] +MAX_EPISODES = 200 +FRESH_TIME = 0.1 + +def build_q_table(n_state, actions): + q_table = pd.DataFrame( + np.zeros((n_state, len(actions))), + np.arange(n_state), + actions + ) + return q_table + +def choose_action(state, q_table): + #epslion - greedy policy + state_action = q_table.loc[state,:] + if np.random.uniform()>EPSILION or (state_action==0).all(): + action_name = np.random.choice(ACTIONS) + else: + action_name = state_action.idxmax() + return action_name + +def get_env_feedback(state, action): + if action=='right': + if state == N_STATE-2: + next_state = 'terminal' + reward = 1 + else: + next_state = state+1 + reward = -0.5 + else: + if state == 0: + next_state = 0 + + else: + next_state = state-1 + reward = -0.5 + return next_state, reward + +def update_env(state,episode, step_counter): + env = ['-'] *(N_STATE-1)+['T'] + if state =='terminal': + print("Episode {}, the total step is {}".format(episode+1, step_counter)) + final_env = ['-'] *(N_STATE-1)+['T'] + return True, step_counter + else: + env[state]='*' + env = ''.join(env) + print(env) + time.sleep(FRESH_TIME) + return False, step_counter + + +def sarsa_learning(): + q_table = build_q_table(N_STATE, ACTIONS) + step_counter_times = [] + for episode in range(MAX_EPISODES): + state = 0 + is_terminal = False + step_counter = 0 + update_env(state, episode, step_counter) + while not is_terminal: + action = choose_action(state,q_table) + next_state, reward = get_env_feedback(state, action) + if next_state != 'terminal': + next_action = choose_action(next_state, q_table) #sarsa update method + else: + next_action = action + next_q = q_table.loc[state, action] + + if next_state == 'terminal': + is_terminal = True + q_target = reward + else: + delta = reward + GAMMA*q_table.loc[next_state,next_action]-q_table.loc[state, action] + q_table.loc[state, action] += ALPHA*delta + state = next_state + is_terminal,steps = update_env(state, episode, step_counter+1) + step_counter+=1 + if is_terminal: + step_counter_times.append(steps) + + return q_table, step_counter_times + +def main(): + q_table, step_counter_times= sarsa_learning() + print("Q table\n{}\n".format(q_table)) + print('end') + + plt.plot(step_counter_times,'g-') + plt.ylabel("steps") + plt.show() + print("The step_counter_times is {}".format(step_counter_times)) + +main() diff --git a/Char00 Conventional Algorithms/gridworld.py b/Char00 Conventional Algorithms/gridworld.py new file mode 100644 index 0000000..4b01c0a --- /dev/null +++ b/Char00 Conventional Algorithms/gridworld.py @@ -0,0 +1,152 @@ + +import numpy as np + +class GridWorld: + + def __init__(self, tot_row, tot_col): + self.action_space_size = 4 + self.world_row = tot_row + self.world_col = tot_col + #The world is a matrix of size row x col x 2 + #The first layer contains the obstacles + #The second layer contains the rewards + #self.world_matrix = np.zeros((tot_row, tot_col, 2)) + self.transition_matrix = np.ones((self.action_space_size, self.action_space_size))/ self.action_space_size + #self.transition_array = np.ones(self.action_space_size) / self.action_space_size + self.reward_matrix = np.zeros((tot_row, tot_col)) + self.state_matrix = np.zeros((tot_row, tot_col)) + self.position = [np.random.randint(tot_row), np.random.randint(tot_col)] + + #def setTransitionArray(self, transition_array): + #if(transition_array.shape != self.transition_array): + #raise ValueError('The shape of the two matrices must be the same.') + #self.transition_array = transition_array + + def setTransitionMatrix(self, transition_matrix): + '''Set the reward matrix. + + The transition matrix here is intended as a matrix which has a line + for each action and the element of the row are the probabilities to + executes each action when a command is given. For example: + [[0.55, 0.25, 0.10, 0.10] + [0.25, 0.25, 0.25, 0.25] + [0.30, 0.20, 0.40, 0.10] + [0.10, 0.20, 0.10, 0.60]] + + This matrix defines the transition rules for all the 4 possible actions. + The first row corresponds to the probabilities of executing each one of + the 4 actions when the policy orders to the robot to go UP. In this case + the transition model says that with a probability of 0.55 the robot will + go UP, with a probaiblity of 0.25 RIGHT, 0.10 DOWN and 0.10 LEFT. + ''' + if(transition_matrix.shape != self.transition_matrix.shape): + raise ValueError('The shape of the two matrices must be the same.') + self.transition_matrix = transition_matrix + + def setRewardMatrix(self, reward_matrix): + '''Set the reward matrix. + + ''' + if(reward_matrix.shape != self.reward_matrix.shape): + raise ValueError('The shape of the matrix does not match with the shape of the world.') + self.reward_matrix = reward_matrix + + def setStateMatrix(self, state_matrix): + '''Set the obstacles in the world. + + The input to the function is a matrix with the + same size of the world + -1 for states which are not walkable. + +1 for terminal states + 0 for all the walkable states (non terminal) + The following matrix represents the 4x3 world + used in the series "dissecting reinforcement learning" + [[0, 0, 0, +1] + [0, -1, 0, +1] + [0, 0, 0, 0]] + ''' + if(state_matrix.shape != self.state_matrix.shape): + raise ValueError('The shape of the matrix does not match with the shape of the world.') + self.state_matrix = state_matrix + + def setPosition(self, index_row=None, index_col=None): + ''' Set the position of the robot in a specific state. + + ''' + if(index_row is None or index_col is None): self.position = [np.random.randint(tot_row), np.random.randint(tot_col)] + else: self.position = [index_row, index_col] + + def render(self): + ''' Print the current world in the terminal. + + O represents the robot position + - respresent empty states. + # represents obstacles + * represents terminal states + ''' + graph = "" + for row in range(self.world_row): + row_string = "" + for col in range(self.world_col): + if(self.position == [row, col]): row_string += u" \u25CB " # u" \u25CC " + else: + if(self.state_matrix[row, col] == 0): row_string += ' - ' + elif(self.state_matrix[row, col] == -1): row_string += ' # ' + elif(self.state_matrix[row, col] == +1): row_string += ' * ' + row_string += '\n' + graph += row_string + print(graph) + + def reset(self, exploring_starts=False): + ''' Set the position of the robot in the bottom left corner. + + It returns the first observation + ''' + if exploring_starts: + while(True): + row = np.random.randint(0, self.world_row) + col = np.random.randint(0, self.world_col) + if(self.state_matrix[row, col] == 0): break + self.position = [row, col] + else: + self.position = [self.world_row-1, 0] + #reward = self.reward_matrix[self.position[0], self.position[1]] + return self.position + + def step(self, action): + ''' One step in the world. + + [observation, reward, done = env.step(action)] + The robot moves one step in the world based on the action given. + The action can be 0=UP, 1=RIGHT, 2=DOWN, 3=LEFT + @return observation the position of the robot after the step + @return reward the reward associated with the next state + @return done True if the state is terminal + ''' + if(action >= self.action_space_size): + raise ValueError('The action is not included in the action space.') + + #Based on the current action and the probability derived + #from the trasition model it chooses a new actio to perform + action = np.random.choice(4, 1, p=self.transition_matrix[int(action),:]) + #action = self.transition_model(action) + + #Generating a new position based on the current position and action + if(action == 0): new_position = [self.position[0]-1, self.position[1]] #UP + elif(action == 1): new_position = [self.position[0], self.position[1]+1] #RIGHT + elif(action == 2): new_position = [self.position[0]+1, self.position[1]] #DOWN + elif(action == 3): new_position = [self.position[0], self.position[1]-1] #LEFT + else: raise ValueError('The action is not included in the action space.') + + #Check if the new position is a valid position + #print(self.state_matrix) + if (new_position[0]>=0 and new_position[0]=0 and new_position[1]= MEMORY_CAPACITY: + dqn.learn() + if done: + print("episode: {} , the episode reward is {}".format(i, round(ep_reward, 3))) + if done: + break + state = next_state + r = copy.copy(reward) + reward_list.append(r) + ax.set_xlim(0,300) + #ax.cla() + ax.plot(reward_list, 'g-', label='total_loss') + plt.pause(0.001) + + +if __name__ == '__main__': + main() diff --git a/Char01 DQN/DQN/pic/finish_episode.jpg b/Char01 DQN/DQN/pic/finish_episode.jpg new file mode 100644 index 0000000000000000000000000000000000000000..b66475d439538d7da0fafdefd22a3edc0decfc64 GIT binary patch literal 35678 zcmdqK1zc6z6EJ${kZzT3kd%^cknT?D5CH)}x>33jknR=%k(L$#>5xVR=`IiT9YQal zT<^c$``$b5-`=y=TPLQT zd;mW`;E)hdV9+2O&Q%Nm00s^Lc{~C@fB*x4qd=g58VL#U@6|Zx^zRG(!HMsd%%xQ&g6|sI8PkX3 z0QACegy{UT^aK4LULpKc7sS1@e;(3O6=@On5nVl_v(+=3jkP9Ylen~vH>?`Qea&^o zMz%ll9PZmN^_qte5Qrv?FVC*V6veMp$LT%(&eA>rys_ybeI%x!IU}G*CKyAPXvs5C zG+AfgqL?Ea1$TBNhGUR{us;rR$NN7~C%`xHpC}M;6&pKG^n>w~jLzh=`|Nfz{bznK 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zP~TNZU$|iKsEkOFFR+aOwr5vc8*lez0LMKkw|;g>Yva&MF&JJO`*3jCzt?!z$JJY&zxbLq%>FHOJlarw*gKEFuba0rG xDDeScCIL8*za46(d7cv&7eBqWOLma?y83eUKkDHVl{D7W*R&0_@~_?r`4`!$@p=FN literal 0 HcmV?d00001 diff --git a/Char10 TD3/TD3.py b/Char10 TD3/TD3.py new file mode 100644 index 0000000..6860929 --- /dev/null +++ b/Char10 TD3/TD3.py @@ -0,0 +1,309 @@ +import argparse +from collections import namedtuple +from itertools import count + +import os, sys, random +import numpy as np + +import gym +import torch +import torch.nn as nn +import torch.nn.functional as F +import torch.optim as optim +from torch.distributions import Normal +from tensorboardX import SummaryWriter + +device = 'cuda' if torch.cuda.is_available() else 'cpu' +parser = argparse.ArgumentParser() + +parser.add_argument('--mode', default='train', type=str) # mode = 'train' or 'test' +parser.add_argument("--env_name", default="Pendulum-v0") # OpenAI gym environment name, BipedalWalker-v2 +parser.add_argument('--tau', default=0.005, type=float) # target smoothing coefficient +parser.add_argument('--target_update_interval', default=1, type=int) +parser.add_argument('--iteration', default=5, type=int) + +parser.add_argument('--learning_rate', default=3e-4, type=float) +parser.add_argument('--gamma', default=0.99, type=int) # discounted factor +parser.add_argument('--capacity', default=50000, type=int) # replay buffer size +parser.add_argument('--num_iteration', default=100000, type=int) # num of games +parser.add_argument('--batch_size', default=100, type=int) # mini batch size +parser.add_argument('--seed', default=1, type=int) + +# optional parameters +parser.add_argument('--num_hidden_layers', default=2, type=int) +parser.add_argument('--sample_frequency', default=256, type=int) +parser.add_argument('--activation', default='Relu', type=str) +parser.add_argument('--render', default=False, type=bool) # show UI or not +parser.add_argument('--log_interval', default=50, type=int) # +parser.add_argument('--load', default=False, type=bool) # load model +parser.add_argument('--render_interval', default=100, type=int) # after render_interval, the env.render() will work +parser.add_argument('--policy_noise', default=0.2, type=float) +parser.add_argument('--noise_clip', default=0.5, type=float) +parser.add_argument('--policy_delay', default=2, type=int) +parser.add_argument('--exploration_noise', default=0.1, type=float) +parser.add_argument('--max_episode', default=2000, type=int) +parser.add_argument('--print_log', default=5, type=int) +args = parser.parse_args() + + + +# Set seeds +# env.seed(args.seed) +# torch.manual_seed(args.seed) +# np.random.seed(args.seed) +device = 'cuda' if torch.cuda.is_available() else 'cpu' +script_name = os.path.basename(__file__) +env = gym.make(args.env_name) + +state_dim = env.observation_space.shape[0] +action_dim = env.action_space.shape[0] +max_action = float(env.action_space.high[0]) +min_Val = torch.tensor(1e-7).float().to(device) # min value + +directory = './exp' + script_name + args.env_name +'./' +''' +Implementation of TD3 with pytorch +Original paper: https://arxiv.org/abs/1802.09477 +Not the author's implementation ! +''' + +class Replay_buffer(): + ''' + Code based on: + https://github.com/openai/baselines/blob/master/baselines/deepq/replay_buffer.py + Expects tuples of (state, next_state, action, reward, done) + ''' + def __init__(self, max_size=args.capacity): + self.storage = [] + self.max_size = max_size + self.ptr = 0 + + def push(self, data): + if len(self.storage) == self.max_size: + self.storage[int(self.ptr)] = data + self.ptr = (self.ptr + 1) % self.max_size + else: + self.storage.append(data) + + def sample(self, batch_size): + ind = np.random.randint(0, len(self.storage), size=batch_size) + x, y, u, r, d = [], [], [], [], [] + + for i in ind: + X, Y, U, R, D = self.storage[i] + x.append(np.array(X, copy=False)) + y.append(np.array(Y, copy=False)) + u.append(np.array(U, copy=False)) + r.append(np.array(R, copy=False)) + d.append(np.array(D, copy=False)) + + return np.array(x), np.array(y), np.array(u), np.array(r).reshape(-1, 1), np.array(d).reshape(-1, 1) + + +class Actor(nn.Module): + + def __init__(self, state_dim, action_dim, max_action): + super(Actor, self).__init__() + + self.fc1 = nn.Linear(state_dim, 400) + self.fc2 = nn.Linear(400, 300) + self.fc3 = nn.Linear(300, action_dim) + + self.max_action = max_action + + def forward(self, state): + a = F.relu(self.fc1(state)) + a = F.relu(self.fc2(a)) + a = torch.tanh(self.fc3(a)) * self.max_action + return a + + +class Critic(nn.Module): + + def __init__(self, state_dim, action_dim): + super(Critic, self).__init__() + + self.fc1 = nn.Linear(state_dim + action_dim, 400) + self.fc2 = nn.Linear(400, 300) + self.fc3 = nn.Linear(300, 1) + + def forward(self, state, action): + state_action = torch.cat([state, action], 1) + + q = F.relu(self.fc1(state_action)) + q = F.relu(self.fc2(q)) + q = self.fc3(q) + return q + + +class TD3(): + def __init__(self, state_dim, action_dim, max_action): + + self.actor = Actor(state_dim, action_dim, max_action).to(device) + self.actor_target = Actor(state_dim, action_dim, max_action).to(device) + self.critic_1 = Critic(state_dim, action_dim).to(device) + self.critic_1_target = Critic(state_dim, action_dim).to(device) + self.critic_2 = Critic(state_dim, action_dim).to(device) + self.critic_2_target = Critic(state_dim, action_dim).to(device) + + self.actor_optimizer = optim.Adam(self.actor.parameters()) + self.critic_1_optimizer = optim.Adam(self.critic_1.parameters()) + self.critic_2_optimizer = optim.Adam(self.critic_2.parameters()) + + self.actor_target.load_state_dict(self.actor.state_dict()) + self.critic_1_target.load_state_dict(self.critic_1.state_dict()) + self.critic_2_target.load_state_dict(self.critic_2.state_dict()) + + self.max_action = max_action + self.memory = Replay_buffer(args.capacity) + self.writer = SummaryWriter(directory) + self.num_critic_update_iteration = 0 + self.num_actor_update_iteration = 0 + self.num_training = 0 + + def select_action(self, state): + state = torch.tensor(state.reshape(1, -1)).float().to(device) + return self.actor(state).cpu().data.numpy().flatten() + + def update(self, num_iteration): + + if self.num_training % 500 == 0: + print("====================================") + print("model has been trained for {} times...".format(self.num_training)) + print("====================================") + for i in range(num_iteration): + x, y, u, r, d = self.memory.sample(args.batch_size) + state = torch.FloatTensor(x).to(device) + action = torch.FloatTensor(u).to(device) + next_state = torch.FloatTensor(y).to(device) + done = torch.FloatTensor(d).to(device) + reward = torch.FloatTensor(r).to(device) + + # Select next action according to target policy: + noise = torch.ones_like(action).data.normal_(0, args.policy_noise).to(device) + noise = noise.clamp(-args.noise_clip, args.noise_clip) + next_action = (self.actor_target(next_state) + noise) + next_action = next_action.clamp(-self.max_action, self.max_action) + + # Compute target Q-value: + target_Q1 = self.critic_1_target(next_state, next_action) + target_Q2 = self.critic_2_target(next_state, next_action) + target_Q = torch.min(target_Q1, target_Q2) + target_Q = reward + ((1 - done) * args.gamma * target_Q).detach() + + # Optimize Critic 1: + current_Q1 = self.critic_1(state, action) + loss_Q1 = F.mse_loss(current_Q1, target_Q) + self.critic_1_optimizer.zero_grad() + loss_Q1.backward() + self.critic_1_optimizer.step() + self.writer.add_scalar('Loss/Q1_loss', loss_Q1, global_step=self.num_critic_update_iteration) + + # Optimize Critic 2: + current_Q2 = self.critic_2(state, action) + loss_Q2 = F.mse_loss(current_Q2, target_Q) + self.critic_2_optimizer.zero_grad() + loss_Q2.backward() + self.critic_2_optimizer.step() + self.writer.add_scalar('Loss/Q2_loss', loss_Q2, global_step=self.num_critic_update_iteration) + # Delayed policy updates: + if i % args.policy_delay == 0: + # Compute actor loss: + actor_loss = - self.critic_1(state, self.actor(state)).mean() + + # Optimize the actor + self.actor_optimizer.zero_grad() + actor_loss.backward() + self.actor_optimizer.step() + self.writer.add_scalar('Loss/actor_loss', actor_loss, global_step=self.num_actor_update_iteration) + for param, target_param in zip(self.actor.parameters(), self.actor_target.parameters()): + target_param.data.copy_(((1- args.tau) * target_param.data) + args.tau * param.data) + + for param, target_param in zip(self.critic_1.parameters(), self.critic_1_target.parameters()): + target_param.data.copy_(((1 - args.tau) * target_param.data) + args.tau * param.data) + + for param, target_param in zip(self.critic_2.parameters(), self.critic_2_target.parameters()): + target_param.data.copy_(((1 - args.tau) * target_param.data) + args.tau * param.data) + + self.num_actor_update_iteration += 1 + self.num_critic_update_iteration += 1 + self.num_training += 1 + + def save(self): + torch.save(self.actor.state_dict(), directory+'actor.pth') + torch.save(self.actor_target.state_dict(), directory+'actor_target.pth') + torch.save(self.critic_1.state_dict(), directory+'critic_1.pth') + torch.save(self.critic_1_target.state_dict(), directory+'critic_1_target.pth') + torch.save(self.critic_2.state_dict(), directory+'critic_2.pth') + torch.save(self.critic_2_target.state_dict(), directory+'critic_2_target.pth') + print("====================================") + print("Model has been saved...") + print("====================================") + + def load(self): + self.actor.load_state_dict(torch.load(directory + 'actor.pth')) + self.actor_target.load_state_dict(torch.load(directory + 'actor_target.pth')) + self.critic_1.load_state_dict(torch.load(directory + 'critic_1.pth')) + self.critic_1_target.load_state_dict(torch.load(directory + 'critic_1_target.pth')) + self.critic_2.load_state_dict(torch.load(directory + 'critic_2.pth')) + self.critic_2_target.load_state_dict(torch.load(directory + 'critic_2_target.pth')) + print("====================================") + print("model has been loaded...") + print("====================================") + + +def main(): + agent = TD3(state_dim, action_dim, max_action) + ep_r = 0 + + if args.mode == 'test': + agent.load() + for i in range(args.iteration): + state = env.reset() + for t in count(): + action = agent.select_action(state) + next_state, reward, done, info = env.step(np.float32(action)) + ep_r += reward + env.render() + if done or t ==2000 : + print("Ep_i \t{}, the ep_r is \t{:0.2f}, the step is \t{}".format(i, ep_r, t)) + break + state = next_state + + elif args.mode == 'train': + print("====================================") + print("Collection Experience...") + print("====================================") + if args.load: agent.load() + for i in range(args.num_iteration): + state = env.reset() + for t in range(2000): + + action = agent.select_action(state) + action = action + np.random.normal(0, args.exploration_noise, size=env.action_space.shape[0]) + action = action.clip(env.action_space.low, env.action_space.high) + next_state, reward, done, info = env.step(action) + ep_r += reward + if args.render and i >= args.render_interval : env.render() + agent.memory.push((state, next_state, action, reward, np.float(done))) + if i+1 % 10 == 0: + print('Episode {}, The memory size is {} '.format(i, len(agent.memory.storage))) + if len(agent.memory.storage) >= args.capacity-1: + agent.update(10) + + state = next_state + if done or t == args.max_episode -1: + agent.writer.add_scalar('ep_r', ep_r, global_step=i) + if i % args.print_log == 0: + print("Ep_i \t{}, the ep_r is \t{:0.2f}, the step is \t{}".format(i, ep_r, t)) + ep_r = 0 + break + + if i % args.log_interval == 0: + agent.save() + + else: + raise NameError("mode wrong!!!") + +if __name__ == '__main__': + main() diff --git a/Char10 TD3/TD3_BipedalWalker-v2.py b/Char10 TD3/TD3_BipedalWalker-v2.py new file mode 100644 index 0000000..4f771d1 --- /dev/null +++ b/Char10 TD3/TD3_BipedalWalker-v2.py @@ -0,0 +1,313 @@ +import argparse +from collections import namedtuple +from itertools import count + +import os, sys, random +import numpy as np + +import gym +import torch +import torch.nn as nn +import torch.nn.functional as F +import torch.optim as optim +from torch.distributions import Normal +from tensorboardX import SummaryWriter + +device = 'cuda' if torch.cuda.is_available() else 'cpu' +parser = argparse.ArgumentParser() + +parser.add_argument('--mode', default='train', type=str) # mode = 'train' or 'test' +# OpenAI gym environment name, # ['BipedalWalker-v2', 'Pendulum-v0'] or any continuous environment +# Note that if you want test in another game, you should fine-tuning. +parser.add_argument("--env_name", default="BipedalWalker-v2") +parser.add_argument('--tau', default=0.005, type=float) # target smoothing coefficient +parser.add_argument('--target_update_interval', default=1, type=int) +parser.add_argument('--test_iteration', default=10, type=int) + +parser.add_argument('--learning_rate', default=3e-4, type=float) +parser.add_argument('--gamma', default=0.99, type=int) # discounted factor +parser.add_argument('--capacity', default=50000, type=int) # replay buffer size +parser.add_argument('--num_iteration', default=100000, type=int) # num of games +parser.add_argument('--batch_size', default=100, type=int) # mini batch size +parser.add_argument('--seed', default=False, type=bool) +parser.add_argument('--random_seed', default=9527, type=int) +# optional parameters +parser.add_argument('--num_hidden_layers', default=2, type=int) +parser.add_argument('--sample_frequency', default=256, type=int) +parser.add_argument('--render', default=False, type=bool) # show UI or not +parser.add_argument('--log_interval', default=50, type=int) # +parser.add_argument('--load', default=False, type=bool) # load model +parser.add_argument('--render_interval', default=100, type=int) # after render_interval, the env.render() will work +parser.add_argument('--policy_noise', default=0.2, type=float) +parser.add_argument('--noise_clip', default=0.5, type=float) +parser.add_argument('--policy_delay', default=2, type=int) +parser.add_argument('--exploration_noise', default=0.1, type=float) +parser.add_argument('--max_episode', default=2000, type=int) +parser.add_argument('--print_log', default=5, type=int) +args = parser.parse_args() + + + + +device = 'cuda' if torch.cuda.is_available() else 'cpu' +script_name = os.path.basename(__file__) +env = gym.make(args.env_name) +if args.seed: + env.seed(args.random_seed) + torch.manual_seed(args.random_seed) + np.random.seed(args.random_seed) + +state_dim = env.observation_space.shape[0] +action_dim = env.action_space.shape[0] +max_action = float(env.action_space.high[0]) +min_Val = torch.tensor(1e-7).float().to(device) # min value + +directory = './exp' + script_name + args.env_name +'./' +''' +Implementation of TD3 with pytorch +Original paper: https://arxiv.org/abs/1802.09477 +Not the author's implementation ! +''' + +class Replay_buffer(): + ''' + Code based on: + https://github.com/openai/baselines/blob/master/baselines/deepq/replay_buffer.py + Expects tuples of (state, next_state, action, reward, done) + ''' + def __init__(self, max_size=args.capacity): + self.storage = [] + self.max_size = max_size + self.ptr = 0 + + def push(self, data): + if len(self.storage) == self.max_size: + self.storage[int(self.ptr)] = data + self.ptr = (self.ptr + 1) % self.max_size + else: + self.storage.append(data) + + def sample(self, batch_size): + ind = np.random.randint(0, len(self.storage), size=batch_size) + x, y, u, r, d = [], [], [], [], [] + + for i in ind: + X, Y, U, R, D = self.storage[i] + x.append(np.array(X, copy=False)) + y.append(np.array(Y, copy=False)) + u.append(np.array(U, copy=False)) + r.append(np.array(R, copy=False)) + d.append(np.array(D, copy=False)) + + return np.array(x), np.array(y), np.array(u), np.array(r).reshape(-1, 1), np.array(d).reshape(-1, 1) + + +class Actor(nn.Module): + + def __init__(self, state_dim, action_dim, max_action): + super(Actor, self).__init__() + + self.fc1 = nn.Linear(state_dim, 400) + self.fc2 = nn.Linear(400, 300) + self.fc3 = nn.Linear(300, action_dim) + + self.max_action = max_action + + def forward(self, state): + a = F.relu(self.fc1(state)) + a = F.relu(self.fc2(a)) + a = torch.tanh(self.fc3(a)) * self.max_action + return a + + +class Critic(nn.Module): + + def __init__(self, state_dim, action_dim): + super(Critic, self).__init__() + + self.fc1 = nn.Linear(state_dim + action_dim, 400) + self.fc2 = nn.Linear(400, 300) + self.fc3 = nn.Linear(300, 1) + + def forward(self, state, action): + state_action = torch.cat([state, action], 1) + + q = F.relu(self.fc1(state_action)) + q = F.relu(self.fc2(q)) + q = self.fc3(q) + return q + + +class TD3(): + def __init__(self, state_dim, action_dim, max_action): + + self.actor = Actor(state_dim, action_dim, max_action).to(device) + self.actor_target = Actor(state_dim, action_dim, max_action).to(device) + self.critic_1 = Critic(state_dim, action_dim).to(device) + self.critic_1_target = Critic(state_dim, action_dim).to(device) + self.critic_2 = Critic(state_dim, action_dim).to(device) + self.critic_2_target = Critic(state_dim, action_dim).to(device) + + self.actor_optimizer = optim.Adam(self.actor.parameters()) + self.critic_1_optimizer = optim.Adam(self.critic_1.parameters()) + self.critic_2_optimizer = optim.Adam(self.critic_2.parameters()) + + self.actor_target.load_state_dict(self.actor.state_dict()) + self.critic_1_target.load_state_dict(self.critic_1.state_dict()) + self.critic_2_target.load_state_dict(self.critic_2.state_dict()) + + self.max_action = max_action + self.memory = Replay_buffer(args.capacity) + self.writer = SummaryWriter(directory) + self.num_critic_update_iteration = 0 + self.num_actor_update_iteration = 0 + self.num_training = 0 + + def select_action(self, state): + state = torch.tensor(state.reshape(1, -1)).float().to(device) + return self.actor(state).cpu().data.numpy().flatten() + + def update(self, num_iteration): + + if self.num_training % 500 == 0: + print("====================================") + print("model has been trained for {} times...".format(self.num_training)) + print("====================================") + for i in range(num_iteration): + x, y, u, r, d = self.memory.sample(args.batch_size) + state = torch.FloatTensor(x).to(device) + action = torch.FloatTensor(u).to(device) + next_state = torch.FloatTensor(y).to(device) + done = torch.FloatTensor(d).to(device) + reward = torch.FloatTensor(r).to(device) + + # Select next action according to target policy: + noise = torch.ones_like(action).data.normal_(0, args.policy_noise).to(device) + noise = noise.clamp(-args.noise_clip, args.noise_clip) + next_action = (self.actor_target(next_state) + noise) + next_action = next_action.clamp(-self.max_action, self.max_action) + + # Compute target Q-value: + target_Q1 = self.critic_1_target(next_state, next_action) + target_Q2 = self.critic_2_target(next_state, next_action) + target_Q = torch.min(target_Q1, target_Q2) + target_Q = reward + ((1 - done) * args.gamma * target_Q).detach() + + # Optimize Critic 1: + current_Q1 = self.critic_1(state, action) + loss_Q1 = F.mse_loss(current_Q1, target_Q) + self.critic_1_optimizer.zero_grad() + loss_Q1.backward() + self.critic_1_optimizer.step() + self.writer.add_scalar('Loss/Q1_loss', loss_Q1, global_step=self.num_critic_update_iteration) + + # Optimize Critic 2: + current_Q2 = self.critic_2(state, action) + loss_Q2 = F.mse_loss(current_Q2, target_Q) + self.critic_2_optimizer.zero_grad() + loss_Q2.backward() + self.critic_2_optimizer.step() + self.writer.add_scalar('Loss/Q2_loss', loss_Q2, global_step=self.num_critic_update_iteration) + # Delayed policy updates: + if i % args.policy_delay == 0: + # Compute actor loss: + actor_loss = - self.critic_1(state, self.actor(state)).mean() + + # Optimize the actor + self.actor_optimizer.zero_grad() + actor_loss.backward() + self.actor_optimizer.step() + self.writer.add_scalar('Loss/actor_loss', actor_loss, global_step=self.num_actor_update_iteration) + for param, target_param in zip(self.actor.parameters(), self.actor_target.parameters()): + target_param.data.copy_(((1- args.tau) * target_param.data) + args.tau * param.data) + + for param, target_param in zip(self.critic_1.parameters(), self.critic_1_target.parameters()): + target_param.data.copy_(((1 - args.tau) * target_param.data) + args.tau * param.data) + + for param, target_param in zip(self.critic_2.parameters(), self.critic_2_target.parameters()): + target_param.data.copy_(((1 - args.tau) * target_param.data) + args.tau * param.data) + + self.num_actor_update_iteration += 1 + self.num_critic_update_iteration += 1 + self.num_training += 1 + + def save(self): + torch.save(self.actor.state_dict(), directory+'actor.pth') + torch.save(self.actor_target.state_dict(), directory+'actor_target.pth') + torch.save(self.critic_1.state_dict(), directory+'critic_1.pth') + torch.save(self.critic_1_target.state_dict(), directory+'critic_1_target.pth') + torch.save(self.critic_2.state_dict(), directory+'critic_2.pth') + torch.save(self.critic_2_target.state_dict(), directory+'critic_2_target.pth') + print("====================================") + print("Model has been saved...") + print("====================================") + + def load(self): + self.actor.load_state_dict(torch.load(directory + 'actor.pth')) + self.actor_target.load_state_dict(torch.load(directory + 'actor_target.pth')) + self.critic_1.load_state_dict(torch.load(directory + 'critic_1.pth')) + self.critic_1_target.load_state_dict(torch.load(directory + 'critic_1_target.pth')) + self.critic_2.load_state_dict(torch.load(directory + 'critic_2.pth')) + self.critic_2_target.load_state_dict(torch.load(directory + 'critic_2_target.pth')) + print("====================================") + print("model has been loaded...") + print("====================================") + + +def main(): + agent = TD3(state_dim, action_dim, max_action) + ep_r = 0 + + if args.mode == 'test': + agent.load() + for i in range(args.test_iteration): + state = env.reset() + for t in count(): + action = agent.select_action(state) + next_state, reward, done, info = env.step(np.float32(action)) + ep_r += reward + env.render() + if done or t ==2000 : + print("Ep_i \t{}, the ep_r is \t{:0.2f}, the step is \t{}".format(i, ep_r, t)) + ep_r = 0 + break + state = next_state + + + elif args.mode == 'train': + print("====================================") + print("Collection Experience...") + print("====================================") + if args.load: agent.load() + for i in range(args.num_iteration): + state = env.reset() + for t in range(2000): + + action = agent.select_action(state) + action = action + np.random.normal(0, args.exploration_noise, size=env.action_space.shape[0]) + action = action.clip(env.action_space.low, env.action_space.high) + next_state, reward, done, info = env.step(action) + ep_r += reward + if args.render and i >= args.render_interval : env.render() + agent.memory.push((state, next_state, action, reward, np.float(done))) + if i+1 % 10 == 0: + print('Episode {}, The memory size is {} '.format(i, len(agent.memory.storage))) + if len(agent.memory.storage) >= args.capacity-1: + agent.update(10) + + state = next_state + if done or t == args.max_episode -1: + agent.writer.add_scalar('ep_r', ep_r, global_step=i) + if i % args.print_log == 0: + print("Ep_i \t{}, the ep_r is \t{:0.2f}, the step is \t{}".format(i, ep_r, t)) + ep_r = 0 + break + + if i % args.log_interval == 0: + agent.save() + + else: + raise NameError("mode wrong!!!") + +if __name__ == '__main__': + main() diff --git a/Char10 TD3/TD3_Pendulum-v0.png 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