Merge pull request #136 from pockerman/refactor_to_v1_api

Add new C++ example documentation

docs/source/ExamplesCpp/rl/rl_example_7.rst

@@ -0,0 +1,86 @@
+Policy iteration  on ``FrozenLake-v0`` (C++)
+=========================================================
+
+
+Code
+----
+
+.. code-block::
+
+	#include "cubeai/base/cubeai_types.h"
+	#include "cubeai/rl/algorithms/dp/policy_iteration.h"
+	#include "cubeai/rl/trainers/rl_serial_agent_trainer.h"
+	#include "cubeai/rl/policies/uniform_discrete_policy.h"
+	#include "cubeai/rl/policies/stochastic_adaptor_policy.h"
+
+	#include "gymfcpp/gymfcpp_types.h"
+	#include "gymfcpp/frozen_lake_env.h"
+	#include "gymfcpp/time_step.h"
+
+	#include <boost/python.hpp>
+
+	#include <iostream>
+
+.. code-block::
+
+	namespace rl_example_7
+	{
+
+	using cubeai::real_t;
+	using cubeai::uint_t;
+	using cubeai::rl::policies::UniformDiscretePolicy;
+	using cubeai::rl::policies::StochasticAdaptorPolicy;
+	using cubeai::rl::algos::dp::PolicyIteration;
+	using cubeai::rl::algos::dp::PolicyIterationConfig;
+	using cubeai::rl::RLSerialAgentTrainer;
+	using cubeai::rl::RLSerialTrainerConfig;
+
+	typedef gymfcpp::TimeStep<uint_t> time_step_type;
+
+	}
+	
+.. code-block::
+
+
+	int main() {
+
+	    using namespace rl_example_7;
+
+	    Py_Initialize();
+	    auto gym_module = boost::python::import("__main__");
+	    auto gym_namespace = gym_module.attr("__dict__");
+
+	    gymfcpp::FrozenLake<4> env("v0", gym_namespace);
+	    env.make();
+
+	    UniformDiscretePolicy policy(env.n_states(), env.n_actions());
+	    StochasticAdaptorPolicy policy_adaptor(env.n_states(), env.n_actions(), policy);
+
+	    PolicyIterationConfig config;
+	    config.gamma = 1.0;
+	    config.n_policy_eval_steps = 100;
+	    config.tolerance = 1.0e-8;
+
+	    PolicyIteration<gymfcpp::FrozenLake<4>, UniformDiscretePolicy, StochasticAdaptorPolicy> algorithm(config,
+		                                                                                              policy, policy_adaptor);
+
+
+	    RLSerialTrainerConfig trainer_config = {10, 10000, 1.0e-8};
+
+	    RLSerialAgentTrainer<gymfcpp::FrozenLake<4>,
+		    PolicyIteration<gymfcpp::FrozenLake<4>,
+		                    UniformDiscretePolicy,
+		                    StochasticAdaptorPolicy>> trainer(trainer_config, algorithm);
+
+	    auto info = trainer.train(env);
+	    std::cout<<info<<std::endl;
+
+	    // save the value function into a csv file
+	    policy_itr.save("policy_iteration_frozen_lake_v0.csv");
+
+	    return 0;
+	}
+
+Results
+-------
+

docs/source/rl_examples.rst

@@ -6,11 +6,12 @@ The following is a list of reinforcement learning examples a user can go through
 .. toctree::
    :maxdepth: 2
 
-   Examples/Dummy/dummy_gent_example
    ExamplesCpp/rl/rl_example_0
+   Examples/Dummy/dummy_gent_example
    ExamplesCpp/rl/rl_example_6
    Examples/Dp/iterative_policy_evaluation_frozen_lake_v0
    Examples/Dp/policy_iteration_frozen_lake_v0
+   ExamplesCpp/rl/rl_example_7
    Examples/Dp/value_iteration_frozen_lake_v0
    Examples/Td/q_learning_frozen_lake_v0
    Examples/Td/q_learning_cliff_walking_v0.rst