def test_run(self):
# Set up environment and policy
env = gym.make('MountainCar-v0')
pol = vcf.DiscreteRandomControl(env.action_space.n)
# Define an agent
phi_1 = vcf.BiasUnit()
td_1 = vcf.TD(len(phi_1))
params_1 = {
'alpha': 0.01,
'gm': 0.999,
'gm_p': 0.999,
'lm': 0.1,
}
agent_1 = vcf.Agent(td_1, phi_1, params_1)
agents = [agent_1]
# Set up the experiment
experiment = vcf.LiveExperiment(env, pol, agents)
# Try running the experiment
num_eps = 10
max_steps = 10
experiment.run(num_eps, max_steps, callbacks=[])
def test_run_with_callbacks(self):
# Set up environment and policy
env = gym.make('MountainCar-v0')
pol = vcf.DiscreteRandomControl(env.action_space.n)
# Define an agent
phi_1 = vcf.BiasUnit()
td_1 = vcf.TD(len(phi_1))
params_1 = {
'alpha': 0.01,
'gm': 0.999,
'gm_p': 0.999,
'lm': 0.1,
}
agent_1 = vcf.Agent(td_1, phi_1, params_1)
agents = [agent_1]
# Set up the experiment
experiment = vcf.LiveExperiment(env, pol, agents)
# Set up testing callbacks
cbk = _CheckCallback()
# Try running the experiment
num_eps = 10
max_steps = 10
experiment.run(num_eps, max_steps, callbacks=[cbk])
# Check that the callbacks ran properly
assert (cbk.experiment_begin > 0)
assert (cbk.experiment_end > 0)
assert (cbk.episode_begin > 0)
assert (cbk.episode_end > 0)
assert (cbk.step_begin > 0)
assert (cbk.step_end > 0)
def test_terminal_context(self):
# Set up the agent
param_funcs = {
'alpha': 0.05,
'gm': vcf.Constant(0.9999, 0),
'gm_p': vcf.Constant(0.9999, 0),
'lm': 0.1
}
phi = vcf.BinaryVector(10)
algo = vcf.TD(len(phi))
agent = vcf.Agent(algo, phi, param_funcs)
# No base context
base_ctx = {}
term_ctx = agent.terminal_context(base_ctx)
assert (isinstance(term_ctx, dict))
assert (term_ctx['done'] == True)
assert (term_ctx['r'] == 0)
assert (all(term_ctx['xp'] == 0))
# Nonsense base context (should still be present)
base_ctx = {'__' + str(i): i**2 for i in range(10)}
term_ctx = agent.terminal_context(base_ctx)
assert (isinstance(term_ctx, dict))
assert (term_ctx['done'] == True)
assert (term_ctx['r'] == 0)
assert (all(term_ctx['xp'] == 0))
assert (all(key in term_ctx for key in base_ctx.keys()))
assert (term_ctx[key] == val for key, val in base_ctx.items())
def test_setup(self):
# Set up the agent
param_funcs = {
'alpha': 0.05,
'gm': vcf.Constant(0.9999, 0),
'gm_p': vcf.Constant(0.9999, 0),
'lm': 0.1
}
phi = vcf.BinaryVector(10)
algo = vcf.TD(len(phi))
agent = vcf.Agent(algo, phi, param_funcs)
# Tile coding for discretization to binary vectors
tiling_1 = vcf.features.BinaryTiling(env.observation_space, 11)
tiling_2 = vcf.features.BinaryTiling(env.observation_space, 19)
tiling_3 = vcf.features.BinaryTiling(env.observation_space, 31)
# Concatenate binary vectors
phi = vcf.Union(tiling_1, tiling_2, tiling_3)
# Define the control (discrete actions Q-learning)
dq = vcf.DiscreteQ(len(phi), na, epsilon=0.002)
dq_params = {
'alpha' : vcf.parameters.EpisodicPowerLaw(0.2, 0.25),
'gm' : 0.9999,
'gm_p' : vcf.Constant(0.9999, 0),
'lm' : vcf.Constant(0.5, 0),
}
control = vcf.Agent(dq, phi, dq_params)
# List of agents to update
learners = [control]
# Set up the experiment
experiment = vcf.LiveExperiment(env, control, learners=learners)
# Set up callbacks
hist_cbk = vcf.callbacks.History()
cbk_lst = [
vcf.callbacks.Progress(),
hist_cbk,
]
# Run the experiment
experiment.run(150, 2000, callbacks=cbk_lst)
v_lm = kappa(ctx)
v_lm_p = kappa_p(ctx)
v_nxt = value_agent.get_value(ctx['obs_p'])
# Compute next "reward"
g_bar = ctx['r'] + v_gm_p * (1 - v_lm_p) * v_nxt
r_bar = g_bar**2 + 2 * v_gm_p * v_lm_p * g_bar * v_nxt
return r_bar
# Set up storage for run data
frames = []
# Perform multiple runs of the given number of episodes
for run in range(num_runs):
# Set up (or reset) the agents
value_agent = vcf.Agent(vcf.algos.TD(num_states), phi,
value_params)
direct_agent = vcf.Agent(vcf.algos.TD(num_states),
phi,
direct_params,
reward_func=direct_reward)
second_agent = vcf.Agent(vcf.algos.TD(num_states),
phi,
second_params,
reward_func=second_moment_reward)
# Set up the experiment
learners = [direct_agent, second_agent, value_agent]
experiment = vcf.LiveExperiment(env, control, learners=learners)
# Set up callbacks to record runs
exclusions = ['x', 'xp']
tiling_2 = vcf.features.BinaryTiling(env.observation_space, 19)
bias = vcf.features.BiasUnit()
# Concatenate binary vectors
phi = vcf.Union(bias, tiling_1, tiling_2)
# Parameters for the agent
td_params = {
'alpha': vcf.parameters.EpisodicPowerLaw(0.15, 0.5),
'gm': vcf.Constant(0.999, 0),
'gm_p': vcf.Constant(0.999, 0),
'lm': vcf.Constant(0.1, 0),
}
# Specify the algorithm
algo = vcf.algos.TD(len(phi))
# Combine into agent
agent = vcf.Agent(algo, phi, td_params)
# List of agents to update
learners = [agent]
# Set up the experiment
experiment = vcf.LiveExperiment(env, control, learners=learners)
# Set up callbacks
hist_cbk = vcf.callbacks.History()
cbk_lst = [
vcf.callbacks.Progress(),
hist_cbk,
]
# Initialize via grid-search
if __name__ == "__main__" and True:
import gym
env = gym.make('SimpleMDP-v0')
ns = env.observation_space.n
na = env.action_space.n
q_params = {
'alpha': vcf.Constant(0.01),
'gm': vcf.Constant(0.999, 0),
'gm_p': vcf.Constant(0.999, 0),
'lm': vcf.Constant(0.01, 0),
}
q_phi = vcf.BinaryVector(ns)
q_algo = vcf.DiscreteQ(len(q_phi), na, epsilon=0.05)
control = vcf.Agent(q_algo, q_phi, q_params)
# Define some other agents that simply learn the value function
phi1 = vcf.BinaryVector(ns)
td_params = {
'alpha': vcf.Constant(0.01),
'gm': vcf.Constant(0.999, 0),
'gm_p': vcf.Constant(0.999, 0),
'lm': vcf.Constant(0.01, 0),
}
td_agent1 = vcf.Agent(vcf.TD(len(phi1)), phi1, td_params)
phi2 = vcf.BiasUnit()
td_params2 = {
'alpha': 0.01,
'gm': 0.9,
