reinforce_monitor.h

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#ifndef REINFORCE_MONITOR_H
#define REINFORCE_MONITOR_H
 
#include "cuberl/base/cubeai_config.h"
 
#ifdef USE_PYTORCH
 
#include "cuberl/base/cuberl_types.h"
#include "cuberl/data_structs/experience_buffer.h"
 
 
#include <vector>
#include <tuple>
 
namespace cuberl {
namespace rl {
namespace algos {
namespace pg {
    
template<typename ActionType, typename StateType>
struct ReinforceMonitor
{
    
    typedef StateType state_type;
    typedef ActionType action_type;
    typedef std::tuple<state_type, // the state observed
                       action_type,  // the action taken
                       real_t, // the reward received
                       bool, // done?
                       torch_tensor_t // log prob
                       > experience_tuple_type;
 
    typedef cuberl::containers::ExperienceBuffer<experience_tuple_type> experience_buffer_type;
    
    std::vector<real_t> rewards;
    std::vector<real_t> policy_loss_values;
    std::vector<uint_t> episode_duration;
    
 
    void reset()noexcept;
    
    template<typename T, uint_t index>
    std::vector<T> 
    get(const std::vector<experience_tuple_type>& experience)const;
    
};
 
 
template<typename ActionType, typename StateType>
template<typename T, uint_t index>
std::vector<T> 
ReinforceMonitor<ActionType, StateType>::get(const std::vector<experience_tuple_type>& experience)const{
    
    std::vector<T> result;
    result.reserve(experience.size());
    
    auto b = experience.begin();
    auto e = experience.end();
    
    for(; b != e; ++b){
        auto item = *b;
        result.push_back(std::get<index>(item));
    }
    
    return result;
}
 
template<typename ActionType, typename StateType>
void 
ReinforceMonitor<ActionType, StateType>::reset()noexcept{
 
    policy_loss_values.clear();
    rewards.clear();
    episode_duration.clear();
}
    
}
}
}
}
 
 
 
 
#endif
#endif