def play_and_train(env: gym.Env, agent: QLearningAgent, t_max=10 ** 4):
""" This function should
- run a full game (for t_max steps), actions given by agent
- train agent whenever possible
- return total reward
"""
total_reward = 0.0
state = env.reset()
for _ in range(t_max):
action = agent.get_action(state)
new_state, reward, done, _ = env.step(action)
total_reward += reward
agent.update(state, action, new_state, reward)
state = new_state
if done:
break
state = new_state
return total_reward
n_actions = env.action_space.n
print('States number = %i, Actions number = %i' % (n_states, n_actions))
# create q learning agent with
# alpha=0.5
# get_legal_actions = lambda s: range(n_actions)
# epsilon=0.1
# discount=0.99
alpha = 0.5
get_legal_actions = lambda s: range(n_actions)
epsilon = 0.1
discount = 0.99
agent = QLearningAgent(alpha, epsilon, discount, get_legal_actions)
plt.figure(figsize=[10, 4])
rewards = []
# Training loop
for i in range(max_iterations):
# Play & train game
# Update rewards
# rewards
# Decay agent epsilon
# agent.epsilon = ?
initial_state = env.reset()
rewards.append(play_and_train(env, agent))
if done:
break
return total_reward
if __name__ == '__main__':
env = gym.make("CartPole-v0").env
env.reset()
n_actions = env.action_space.n
print(env.observation_space.high)
print(env.observation_space.low)
print('CartPole state: %s' % (env.reset()))
agent = QLearningAgent(0.3, 0.5, 1.0, lambda s: range(n_actions))
# (x, x', theta, theta')
state_bins = [ # Cart position.
discretize_range(-2.4, 2.4, 2),
# Cart velocity.
discretize_range(-3.0, 3.0, 2),
# Pole angle.
discretize_range(-0.5, 0.5, 7),
# Tip velocity.
discretize_range(-2.0, 2.0, 7)
]
max_bins = max(len(bin) for bin in state_bins)
rewards = []
for i in range(2000):
env = gym.make('Taxi-v3')
env.reset()
env.render()
n_states = env.observation_space.n
n_actions = env.action_space.n
print('States number = %i, Actions number = %i' % (n_states, n_actions))
# create q learning agent with
# alpha=0.5
# get_legal_actions = lambda s: range(n_actions)
# epsilon=0.1
# discount=0.99
agent = QLearningAgent(0.5,0.1,0.99,lambda s: range(n_actions))
plt.figure(figsize=[10, 4])
rewards = []
# Training loop
for i in range(max_iterations):
total_r = play_and_train(env,agent)
rewards.append(total_r)
# Play & train game
# Update rewards
# rewards
# Decay agent epsilon
# agent.epsilon = ?
break
return total_reward
if __name__ == '__main__':
env = gym.make("CartPole-v0").env
env.reset()
n_actions = env.action_space.n
print(env.observation_space.high)
print(env.observation_space.low)
print('CartPole state: %s' % (env.reset()))
agent = QLearningAgent(alpha=0.3,
epsilon=0.5,
discount=1.0,
get_legal_actions=lambda s: range(n_actions))
# (x, x', theta, theta')
state_bins = [ # Cart position.
discretize_range(-2.4, 2.4, 2),
# Cart velocity.
discretize_range(-3.0, 3.0, 2),
# Pole angle.
discretize_range(-0.5, 0.5, 7),
# Tip velocity.
discretize_range(-2.0, 2.0, 7)
]
max_bins = max(len(bin) for bin in state_bins)
rewards = []
env = CliffWalkingEnv()
env.reset()
env.render()
n_states = env.nS
n_actions = env.nA
print('States number = %i, Actions number = %i' % (n_states, n_actions))
# create q learning agent with
alpha = 0.5
get_legal_actions = lambda s: range(n_actions)
epsilon = 0.2
discount = 0.99
agent = QLearningAgent(alpha, epsilon, discount, getActionRange)
agent2 = SarsaAgent(alpha, epsilon, discount, getActionRange)
plt.figure(figsize=[10, 4])
rewards1 = []
rewards2 = []
# Training loop
for i in range(max_iterations):
# Play & train game
rewards1.append(play_and_train(env, agent))
rewards2.append(play_and_train(env, agent2))
if (i + 1) % 100 == 0:
agent.epsilon = max(agent.epsilon * 0.99, 0.00001)
agent2.epsilon = max(agent2.epsilon * 0.99, 0.00001)
# agent.alpha = max(agent.alpha * 0.99, 0.00001)
env = gym.make('Taxi-v2').env
env.reset()
env.render()
n_states = env.observation_space.n
n_actions = env.action_space.n
print('States number = %i, Actions number = %i' % (n_states, n_actions))
# create q learning agent with
# alpha=0.5
# get_legal_actions = lambda s: range(n_actions)
# epsilon=0.1
# discount=0.99
agent = QLearningAgent(alpha=0.5, get_legal_actions=lambda s: range(n_actions), discount=0.99, epsilon=0.1)
plt.figure(figsize=[10, 4])
rewards = []
# Training loop
for i in range(max_iterations):
# Play & train game
# Update rewards
# rewards
rewards.append(play_and_train(env, agent))
# Decay agent epsilon
if i % 100 == 0:
env.reset()
env.render()
n_states = env.observation_space.n
n_actions = env.action_space.n
print('States number = %i, Actions number = %i' % (n_states, n_actions))
# create q learning agent with
# alpha=0.5
# get_legal_actions = lambda s: range(n_actions)
# epsilon=0.1
# discount=0.99
epsilon = 0.1
agent = QLearningAgent(0.5, epsilon, 0.99, lambda s: range(n_actions))
plt.figure(figsize=[10, 4])
rewards = []
# Training loop
for i in range(max_iterations):
# Play & train game
# Update rewards
# rewards
result = play_and_train(env, agent)
rewards.append(result)
# Decay agent epsilon
# agent.epsilon = ?
