def main():
# Init loggers
log.set_level("fine")
log.set_sync(False)
agent_log.set_level("fine")
agent_log.set_sync(False)
ure_logger().set_level("fine")
ure_logger().set_sync(False)
# Set main atomspace
atomspace = AtomSpace()
set_default_atomspace(atomspace)
# Wrap environment
wrapped_env = CartPoleWrapper(env, atomspace)
# Instantiate CartPoleAgent, and tune parameters
cpa = FixedCartPoleAgent(wrapped_env, atomspace)
cpa.delta = 1.0e-16
# Run control loop
while not cpa.control_cycle():
wrapped_env.render()
time.sleep(0.1)
log.info("cycle_count = {}".format(cpa.cycle_count))
log_msg(agent_log, f"The final reward is {cpa.accumulated_reward}.")
def main():
# Init loggers
log.set_level("fine")
log.set_sync(False)
agent_log.set_level("fine")
agent_log.set_sync(False)
ure_logger().set_level("fine")
ure_logger().set_sync(False)
# Set main atomspace
atomspace = AtomSpace()
set_default_atomspace(atomspace)
# Wrap environment
wrapped_env = CartPoleWrapper(env)
# Instantiate CartPoleAgent, and tune parameters
cpa = CartPoleAgent(wrapped_env)
cpa.delta = 1.0e-16
# Run control loop
while cpa.step():
time.sleep(0.1)
log.info("step_count = {}".format(cpa.step_count))
print(f"The final reward is {cpa.accumulated_reward}.")
def load_opencog_modules(self):
# Init loggers
log.set_level("debug")
# log.set_sync(True)
agent_log.set_level("debug")
# agent_log.set_sync(True)
ure_logger().set_level("info")
# ure_logger().set_sync(True)
# Load miner
scheme_eval(self.atomspace, "(use-modules (opencog miner))")
scheme_eval(self.atomspace, "(miner-logger-set-level! \"fine\")")
# scheme_eval(self.atomspace, "(miner-logger-set-sync! #t)")
# Load PLN
scheme_eval(self.atomspace, "(use-modules (opencog pln))")
# scheme_eval(self.atomspace, "(pln-load-rule 'predictive-implication-scope-direct-introduction)")
scheme_eval(self.atomspace, "(pln-load-rule 'predictive-implication-scope-direct-evaluation)")
# No need of predictive implication for now
# scheme_eval(self.atomspace, "(pln-load-rule 'predictive-implication-direct-evaluation)")
scheme_eval(self.atomspace, "(pln-log-atomspace)")
# Create Goal
pgoal = EvaluationLink(PredicateNode("Reward"), NumberNode("1"))
ngoal = EvaluationLink(PredicateNode("Reward"), NumberNode("0"))
# Call super ctor
OpencogAgent.__init__(self, env, action_space, pgoal, ngoal)
if __name__ == "__main__":
# Init loggers
log.set_level("debug")
log.set_sync(False)
agent_log.set_level("fine")
agent_log.set_sync(False)
ure_logger().set_level("debug")
ure_logger().set_sync(False)
# Set main atomspace
atomspace = AtomSpace()
set_default_atomspace(atomspace)
# Wrap environment
wrapped_env = ChaseWrapper(env)
# ChaseAgent
ca = ChaseAgent(wrapped_env)
# Training/learning loop
lt_iterations = 2 # Number of learning-training iterations
lt_period = 200 # Duration of a learning-training iteration
self.monoaction_general_succeedent_mining = False
self.polyaction_mining = False
self.temporal_deduction = False
if __name__ == "__main__":
# Set main atomspace
atomspace = AtomSpace()
set_default_atomspace(atomspace)
# Init loggers
log.set_level("info")
# log.set_sync(True)
agent_log.set_level("debug")
# agent_log.set_sync(True)
ure_logger().set_level("debug")
# ure_logger().set_sync(True)
miner_log = MinerLogger(atomspace)
miner_log.set_level("debug")
# miner_log.set_sync(True)
# Wrap environment
wrapped_env = ChaseWrapper(env)
# ChaseAgent
ca = ChaseAgent(wrapped_env)
# Training/learning loop
lt_iterations = 2 # Number of learning-training iterations
lt_period = 200 # Duration of a learning-training iteration
for i in range(lt_iterations):
self.cogscm_maximum_shannon_entropy = 1
self.cogscm_maximum_differential_entropy = 0
self.cogscm_maximum_variables = 0
if __name__ == "__main__":
# Set main atomspace
atomspace = AtomSpace()
set_default_atomspace(atomspace)
# Init loggers
log.set_level("info")
# log.set_sync(True)
agent_log.set_level("info")
# agent_log.set_sync(True)
ure_logger().set_level("info")
# ure_logger().set_sync(True)
miner_log = MinerLogger(atomspace)
miner_log.set_level("info")
# miner_log.set_sync(True)
# Wrap environment
wrapped_env = ChaseWrapper(env, atomspace)
# ChaseAgent
cag = ChaseAgent(wrapped_env, atomspace)
# Log all parameters of cag, useful for debugging
cag.log_parameters(level="debug")
# Training/learning loop