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testpriodqn.cpp
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/*
* testpriodqn.cpp
*
* Created on: Aug 26, 2021
* Author: zf
*/
#include "alg/cnn/priodqn.hpp"
#include "gymtest/env/airenv.h"
#include "gymtest/env/lunarenv.h"
#include "gymtest/cnnnets/airnets/aircnnnet.h"
#include "gymtest/cnnnets/airnets/airacbmnet.h"
#include "gymtest/cnnnets/airnets/airacnet.h"
#include "gymtest/cnnnets/airnets/airacbmsmallkernelnet.h"
#include "gymtest/cnnnets/lunarnets/cartacnet.h"
#include "gymtest/cnnnets/lunarnets/cartnet.h"
#include "gymtest/cnnnets/lunarnets/cartqnet.h"
#include "gymtest/cnnnets/airnets/airachonet.h"
#include "gymtest/train/rawpolicy.h"
#include "gymtest/train/softmaxpolicy.h"
#include "probeenvs/ProbeEnvWrapper.h"
#include <torch/torch.h>
#include <log4cxx/logger.h>
#include <log4cxx/basicconfigurator.h>
#include <log4cxx/consoleappender.h>
#include <log4cxx/simplelayout.h>
#include <log4cxx/logmanager.h>
#include <vector>
#include "alg/utils/dqnoption.h"
namespace {
log4cxx::LoggerPtr logger(log4cxx::Logger::getLogger("dqntest"));
const torch::Device deviceType = torch::kCUDA;
void testProbe(const int epochNum) {
const int batchSize = 1;
const int inputNum = 4;
const int envId = 5;
const int outputNum = 2;
const int envNum = batchSize;
ProbeEnvWrapper env(inputNum, envId, envNum);
ProbeEnvWrapper testEnv(inputNum, envId, envNum);
CartFcQNet model(inputNum, outputNum);
model.to(deviceType);
CartFcQNet targetModel(inputNum, outputNum);
targetModel.to(deviceType);
torch::optim::Adam optimizer(model.parameters(), torch::optim::AdamOptions(1e-4));
LOG4CXX_INFO(logger, "Model ready");
at::IntArrayRef inputShape{envNum, 4};
DqnOption option(inputShape, deviceType);
//env
option.envNum = envNum;
option.envStep = 4;
option.multiLifes = false;
//target model
option.targetUpdateStep = 100;
option.tau = 1;
//prio
option.rbCap = 1024 * 8; //Cap to be 2^x
option.pbAlpha = 0.6;
option.pbBetaBegin = 0.4;
option.pbBetaEnd = 1;
option.pbEpsilon = 1e-6;
option.pbBetaPart = 1;
//explore
option.exploreBegin = 1;
option.exploreEnd = 0.01;
option.explorePart = 0.8;
//input
option.inputScale = 1;
option.rewardScale = 1;
option.rewardMin = -1; //TODO: reward may not require clip
option.rewardMax = 1;
option.gamma = 0.99;
//grad
option.batchSize = 16;
option.startStep = 100;
option.maxGradNormClip = 1;
//test
option.toTest = true;
option.testGapEp = 1000;
//log
option.logInterval = 100;
option.tensorboardLogPath = "./logs/prio_testprobe/tfevents.pb";
//model
option.saveModel = false;
option.savePathPrefix = "???";
option.loadModel = false;
option.loadOptimizer = false;
RawPolicy policy(option.exploreBegin, outputNum);
PrioDqn<CartFcQNet, ProbeEnvWrapper, RawPolicy, torch::optim::Adam> dqn(model, targetModel, env, testEnv, policy, optimizer, option);
dqn.trainDouble(epochNum);
}
void testCart(const int epochNum) {
const std::string envName = "CartPole-v0";
const int clientNum = 1; //8
const int outputNum = 2;
const int inputNum = 4;
const int testClientNum = 4;
std::string serverAddr = "tcp://127.0.0.1:10201";
LOG4CXX_DEBUG(logger, "To connect to " << serverAddr);
LunarEnv env(serverAddr, envName, clientNum);
env.init();
std::string testServerAddr = "tcp://127.0.0.1:10202";
LOG4CXX_DEBUG(logger, "To connect to " << testServerAddr);
LunarEnv testEnv(testServerAddr, envName, testClientNum);
testEnv.init();
LOG4CXX_INFO(logger, "Env " << envName << " ready");
CartFcQNet model(inputNum, outputNum);
model.to(deviceType);
CartFcQNet targetModel(inputNum, outputNum);
targetModel.to(deviceType);
torch::optim::Adam optimizer(model.parameters(), torch::optim::AdamOptions(3e-4));
LOG4CXX_INFO(logger, "Model ready");
at::IntArrayRef inputShape{clientNum, 4};
at::IntArrayRef testInputShape{testClientNum, 4};
DqnOption option(inputShape, testInputShape, deviceType);
//env
option.envNum = clientNum;
option.envStep = 4;
option.multiLifes = false;
//target model
option.targetUpdateStep = 200;
option.tau = 1;
//prio
option.rbCap = 8192 * 8; //Cap to be 2^x
option.pbAlpha = 0.6;
option.pbBetaBegin = 0.4;
option.pbBetaEnd = 1;
option.pbEpsilon = 1e-6;
option.pbBetaPart = 0.9;
//explore
option.exploreBegin = 1;
option.exploreEnd = 0.01;
option.explorePart = 0.8;
//input
option.inputScale = 1;
option.rewardScale = 1;
option.rewardMin = -1; //TODO: reward may not require clip
option.rewardMax = 1;
option.gamma = 0.99;
//grad
option.batchSize = 32;
option.startStep = 1000;
option.maxGradNormClip = 1;
//test
option.toTest = true;
option.testGapEp = 1000;
option.testEp = testClientNum;
option.testBatch = testClientNum;
//log
option.logInterval = 100;
option.tensorboardLogPath = "./logs/prio_testcart/tfevents.pb";
//model
option.saveModel = false;
option.savePathPrefix = "???";
option.loadModel = false;
option.loadOptimizer = false;
RawPolicy policy(option.exploreBegin, outputNum);
PrioDqn<CartFcQNet, LunarEnv, RawPolicy, torch::optim::Adam> dqn(model, targetModel, env, testEnv, policy, optimizer, option);
dqn.trainDouble(epochNum);
}
void testPong(const int epochNum) {
const std::string envName = "PongNoFrameskip-v4";
const int outputNum = 6;
const int clientNum = 1;
const int testClientNum = 4;
std::string serverAddr = "tcp://127.0.0.1:10201";
LOG4CXX_DEBUG(logger, "To connect to " << serverAddr);
AirEnv env(serverAddr, envName, clientNum);
env.init();
std::string testServerAddr = "tcp://127.0.0.1:10202";
LOG4CXX_DEBUG(logger, "To connect to " << testServerAddr);
AirEnv testEnv(testServerAddr, envName, testClientNum);
testEnv.init();
LOG4CXX_INFO(logger, "Env " << envName << " ready");
AirCnnNet model(outputNum);
model.to(deviceType);
AirCnnNet targetModel(outputNum);
targetModel.to(deviceType);
// torch::optim::Adagrad optimizer(model.parameters(), torch::optim::AdagradOptions(1e-3)); //rmsprop: 0.00025
// torch::optim::RMSprop optimizer(model.parameters(), torch::optim::RMSpropOptions(0.00025).eps(0.01).alpha(0.95));
torch::optim::Adam optimizer(model.parameters(), torch::optim::AdamOptions(3e-4));
LOG4CXX_INFO(logger, "Model ready");
at::IntArrayRef inputShape{clientNum, 4, 84, 84};
at::IntArrayRef testInputShape{testClientNum, 4, 84, 84};
DqnOption option(inputShape, testInputShape, deviceType);
//env
option.envNum = clientNum;
option.envStep = 8;
option.multiLifes = false;
//target model
option.targetUpdateStep = 1000;
option.tau = 1;
//prio
option.rbCap = 262144; //Cap to be 2^x
option.pbAlpha = 0.6;
option.pbBetaBegin = 0.4;
option.pbBetaEnd = 1;
option.pbEpsilon = 1e-6;
option.pbBetaPart = 0.9;
//explore
option.exploreBegin = 0.6;
option.exploreEnd = 0.01;
option.explorePart = 0.8;
//input
option.inputScale = 255;
option.rewardScale = 1;
option.rewardMin = -1; //TODO: reward may not require clip
option.rewardMax = 1;
option.gamma = 0.99;
//grad
option.batchSize = 64;
option.startStep = 8192;
option.maxGradNormClip = 1;
//test
option.toTest = true;
option.testGapEp = 5000;
option.testEp = testClientNum;
option.testBatch = testClientNum;
//log
option.logInterval = 1000;
option.tensorboardLogPath = "./logs/prio_testpong/tfevents.pb";
//model
option.saveThreshold = -20;
option.saveStep = 1;
option.saveModel = false;
option.savePathPrefix = "???";
option.loadModel = false;
option.loadOptimizer = false;
RawPolicy policy(option.exploreBegin, outputNum);
PrioDqn<AirCnnNet, AirEnv, RawPolicy, torch::optim::Adam> dqn(model, targetModel, env, testEnv, policy, optimizer, option);
dqn.trainDouble(epochNum);
}
void testPongDouble(const int epochNum) {
const std::string envName = "PongNoFrameskip-v4";
const int outputNum = 6;
const int clientNum = 1;
const int testClientNum = 4;
std::string serverAddr = "tcp://127.0.0.1:10201";
LOG4CXX_DEBUG(logger, "To connect to " << serverAddr);
AirEnv env(serverAddr, envName, clientNum);
env.init();
std::string testServerAddr = "tcp://127.0.0.1:10202";
LOG4CXX_DEBUG(logger, "To connect to " << testServerAddr);
AirEnv testEnv(testServerAddr, envName, testClientNum);
testEnv.init();
LOG4CXX_INFO(logger, "Env " << envName << " ready");
AirCnnNet model(outputNum);
model.to(deviceType);
AirCnnNet targetModel(outputNum);
targetModel.to(deviceType);
// torch::optim::Adagrad optimizer(model.parameters(), torch::optim::AdagradOptions(1e-3)); //rmsprop: 0.00025
// torch::optim::RMSprop optimizer(model.parameters(), torch::optim::RMSpropOptions(0.00025).eps(0.01).alpha(0.95));
torch::optim::Adam optimizer(model.parameters(), torch::optim::AdamOptions(3e-4));
LOG4CXX_INFO(logger, "Model ready");
at::IntArrayRef inputShape{clientNum, 4, 84, 84};
at::IntArrayRef testInputShape{testClientNum, 4, 84, 84};
DqnOption option(inputShape, testInputShape, deviceType);
//env
option.envNum = clientNum;
option.envStep = 8;
option.multiLifes = false;
//target model
option.targetUpdateStep = 2000;
option.tau = 1;
//prio
option.rbCap = 262144; //Cap to be 2^x, 262144
option.pbAlpha = 0.6;
option.pbBetaBegin = 0.4;
option.pbBetaEnd = 1;
option.pbEpsilon = 1e-5;
option.pbBetaPart = 0.9;
//explore
option.exploreBegin = 0.8;
option.exploreEnd = 0.01;
option.explorePart = 0.8;
//input
option.inputScale = 255;
option.rewardScale = 1;
option.rewardMin = -1; //TODO: reward may not require clip
option.rewardMax = 1;
option.gamma = 0.99;
//grad
option.batchSize = 64; //32
option.startStep = 1000; //1000
option.maxGradNormClip = 1;
//test
option.toTest = true;
option.testGapEp = 5000;
option.testEp = testClientNum;
option.testBatch = testClientNum;
//log
option.logInterval = 100;
option.tensorboardLogPath = "./logs/prio_testpongdouble/tfevents.pb";
//model
option.saveThreshold = -20;
option.saveStep = 1;
option.saveModel = false;
option.savePathPrefix = "???";
option.loadModel = false;
option.loadOptimizer = false;
RawPolicy policy(option.exploreBegin, outputNum);
PrioDqn<AirCnnNet, AirEnv, RawPolicy, torch::optim::Adam> dqn(model, targetModel, env, testEnv, policy, optimizer, option);
dqn.trainDouble(epochNum);
}
//TODO: envStep = 1; lr = 1e-4; step = 1000000
void testPongDouble1(const int epochNum) {
const std::string envName = "PongNoFrameskip-v4";
const int outputNum = 6;
const int clientNum = 1;
const int testClientNum = 4;
std::string serverAddr = "tcp://127.0.0.1:10201";
LOG4CXX_DEBUG(logger, "To connect to " << serverAddr);
AirEnv env(serverAddr, envName, clientNum);
env.init();
std::string testServerAddr = "tcp://127.0.0.1:10202";
LOG4CXX_DEBUG(logger, "To connect to " << testServerAddr);
AirEnv testEnv(testServerAddr, envName, testClientNum);
testEnv.init();
LOG4CXX_INFO(logger, "Env " << envName << " ready");
AirCnnNet model(outputNum);
model.to(deviceType);
AirCnnNet targetModel(outputNum);
targetModel.to(deviceType);
// torch::optim::Adagrad optimizer(model.parameters(), torch::optim::AdagradOptions(1e-3)); //rmsprop: 0.00025
// torch::optim::RMSprop optimizer(model.parameters(), torch::optim::RMSpropOptions(0.00025).eps(0.01).alpha(0.95));
torch::optim::Adam optimizer(model.parameters(), torch::optim::AdamOptions(3e-4));
LOG4CXX_INFO(logger, "Model ready");
at::IntArrayRef inputShape{clientNum, 4, 84, 84};
at::IntArrayRef testInputShape{testClientNum, 4, 84, 84};
DqnOption option(inputShape, testInputShape, deviceType);
//env
option.envNum = clientNum;
option.envStep = 8;
option.multiLifes = false;
//target model
option.targetUpdateStep = 2000;
option.tau = 1;
//prio
option.rbCap = 262144; //Cap to be 2^x, 262144
option.pbAlpha = 0.6;
option.pbBetaBegin = 0.4;
option.pbBetaEnd = 1;
option.pbEpsilon = 1e-5;
option.pbBetaPart = 0.3;
//explore
option.exploreBegin = 1;
option.exploreEnd = 0.01;
option.explorePart = 0.3;
//input
option.inputScale = 255;
option.rewardScale = 1;
option.rewardMin = -1; //TODO: reward may not require clip
option.rewardMax = 1;
option.gamma = 0.99;
//grad
option.batchSize = 64; //32
option.startStep = 1000; //1000
option.maxGradNormClip = 1;
//test
option.toTest = true;
option.testGapEp = 5000;
option.testEp = testClientNum;
option.testBatch = testClientNum;
//log
option.logInterval = 100;
option.tensorboardLogPath = "./logs/prio_testpongdouble1/tfevents.pb";
//model
option.saveThreshold = -20;
option.saveStep = 1;
option.saveModel = false;
option.savePathPrefix = "???";
option.loadModel = false;
option.loadOptimizer = false;
RawPolicy policy(option.exploreBegin, outputNum);
PrioDqn<AirCnnNet, AirEnv, RawPolicy, torch::optim::Adam> dqn(model, targetModel, env, testEnv, policy, optimizer, option);
dqn.trainDouble(epochNum);
}
void testPongDouble2(const int epochNum) {
const std::string envName = "PongNoFrameskip-v4";
const int outputNum = 6;
const int clientNum = 1;
const int testClientNum = 4;
std::string serverAddr = "tcp://127.0.0.1:10203";
LOG4CXX_DEBUG(logger, "To connect to " << serverAddr);
AirEnv env(serverAddr, envName, clientNum);
env.init();
std::string testServerAddr = "tcp://127.0.0.1:10204";
LOG4CXX_DEBUG(logger, "To connect to " << testServerAddr);
AirEnv testEnv(testServerAddr, envName, testClientNum);
testEnv.init();
LOG4CXX_INFO(logger, "Env " << envName << " ready");
AirCnnNet model(outputNum);
model.to(deviceType);
AirCnnNet targetModel(outputNum);
targetModel.to(deviceType);
// torch::optim::Adagrad optimizer(model.parameters(), torch::optim::AdagradOptions(1e-3)); //rmsprop: 0.00025
// torch::optim::RMSprop optimizer(model.parameters(), torch::optim::RMSpropOptions(0.00025).eps(0.01).alpha(0.95));
torch::optim::Adam optimizer(model.parameters(), torch::optim::AdamOptions(1e-4));
LOG4CXX_INFO(logger, "Model ready");
at::IntArrayRef inputShape{clientNum, 4, 84, 84};
at::IntArrayRef testInputShape{testClientNum, 4, 84, 84};
DqnOption option(inputShape, testInputShape, deviceType);
//env
option.envNum = clientNum;
option.envStep = 1;
option.multiLifes = false;
//target model
option.targetUpdateStep = 2000;
option.tau = 1;
//prio
option.rbCap = 131072; //Cap to be 2^x, 262144
option.pbAlpha = 0.6;
option.pbBetaBegin = 0.4;
option.pbBetaEnd = 1;
option.pbEpsilon = 1e-5;
option.pbBetaPart = 0.3;
//explore
option.exploreBegin = 1;
option.exploreEnd = 0.01;
option.explorePart = 0.3;
//input
option.inputScale = 255;
option.rewardScale = 1;
option.rewardMin = -1; //TODO: reward may not require clip
option.rewardMax = 1;
option.gamma = 0.99;
//grad
option.batchSize = 32; //32
option.startStep = 10000; //1000
option.maxGradNormClip = 1;
//test
option.toTest = true;
option.testGapEp = 5000;
option.testEp = testClientNum;
option.testBatch = testClientNum;
//log
option.logInterval = 100;
option.tensorboardLogPath = "./logs/prio_testpongdouble2/tfevents.pb";
//model
option.saveThreshold = -20;
option.saveStep = 1;
option.saveModel = false;
option.savePathPrefix = "???";
option.loadModel = false;
option.loadOptimizer = false;
RawPolicy policy(option.exploreBegin, outputNum);
PrioDqn<AirCnnNet, AirEnv, RawPolicy, torch::optim::Adam> dqn(model, targetModel, env, testEnv, policy, optimizer, option);
dqn.trainDouble(epochNum);
}
void testPongDqn(const int epochNum) {
const std::string envName = "PongNoFrameskip-v4";
const int outputNum = 6;
const int clientNum = 1;
const int testClientNum = 4;
std::string serverAddr = "tcp://127.0.0.1:10201";
LOG4CXX_DEBUG(logger, "To connect to " << serverAddr);
AirEnv env(serverAddr, envName, clientNum);
env.init();
std::string testServerAddr = "tcp://127.0.0.1:10202";
LOG4CXX_DEBUG(logger, "To connect to " << testServerAddr);
AirEnv testEnv(testServerAddr, envName, testClientNum);
testEnv.init();
LOG4CXX_INFO(logger, "Env " << envName << " ready");
AirCnnNet model(outputNum);
model.to(deviceType);
AirCnnNet targetModel(outputNum);
targetModel.to(deviceType);
// torch::optim::Adagrad optimizer(model.parameters(), torch::optim::AdagradOptions(1e-3)); //rmsprop: 0.00025
// torch::optim::RMSprop optimizer(model.parameters(), torch::optim::RMSpropOptions(0.00025).eps(0.01).alpha(0.95));
torch::optim::Adam optimizer(model.parameters(), torch::optim::AdamOptions(1e-4));
LOG4CXX_INFO(logger, "Model ready");
at::IntArrayRef inputShape{clientNum, 4, 84, 84};
at::IntArrayRef testInputShape{testClientNum, 4, 84, 84};
DqnOption option(inputShape, testInputShape, deviceType);
//env
option.envNum = clientNum;
option.envStep = 1;
option.multiLifes = false;
//target model
option.targetUpdateStep = 2000;
option.tau = 1;
//prio
option.rbCap = 131072; //Cap to be 2^x, 262144
option.pbAlpha = 0.6;
option.pbBetaBegin = 0.4;
option.pbBetaEnd = 1;
option.pbEpsilon = 1e-5;
option.pbBetaPart = 0.3;
//explore
option.exploreBegin = 1;
option.exploreEnd = 0.01;
option.explorePart = 0.3;
//input
option.inputScale = 255;
option.rewardScale = 1;
option.rewardMin = -1; //TODO: reward may not require clip
option.rewardMax = 1;
option.gamma = 0.99;
//grad
option.batchSize = 32; //32
option.startStep = 10000; //1000
option.maxGradNormClip = 1;
//test
option.toTest = true;
option.testGapEp = 5000;
option.testEp = testClientNum;
option.testBatch = testClientNum;
//log
option.logInterval = 100;
option.tensorboardLogPath = "./logs/prio_testpongdqn/tfevents.pb";
//model
option.saveThreshold = -20;
option.saveStep = 1;
option.saveModel = false;
option.savePathPrefix = "???";
option.loadModel = false;
option.loadOptimizer = false;
RawPolicy policy(option.exploreBegin, outputNum);
PrioDqn<AirCnnNet, AirEnv, RawPolicy, torch::optim::Adam> dqn(model, targetModel, env, testEnv, policy, optimizer, option);
dqn.trainDqn(epochNum);
}
void testPongDqn1(const int epochNum) {
const std::string envName = "PongNoFrameskip-v4";
const int outputNum = 6;
const int clientNum = 1;
const int testClientNum = 4;
std::string serverAddr = "tcp://127.0.0.1:10201";
LOG4CXX_DEBUG(logger, "To connect to " << serverAddr);
AirEnv env(serverAddr, envName, clientNum);
env.init();
std::string testServerAddr = "tcp://127.0.0.1:10202";
LOG4CXX_DEBUG(logger, "To connect to " << testServerAddr);
AirEnv testEnv(testServerAddr, envName, testClientNum);
testEnv.init();
LOG4CXX_INFO(logger, "Env " << envName << " ready");
AirCnnNet model(outputNum);
model.to(deviceType);
AirCnnNet targetModel(outputNum);
targetModel.to(deviceType);
// torch::optim::Adagrad optimizer(model.parameters(), torch::optim::AdagradOptions(1e-3)); //rmsprop: 0.00025
// torch::optim::RMSprop optimizer(model.parameters(), torch::optim::RMSpropOptions(0.00025).eps(0.01).alpha(0.95));
torch::optim::Adam optimizer(model.parameters(), torch::optim::AdamOptions(1e-4));
LOG4CXX_INFO(logger, "Model ready");
at::IntArrayRef inputShape{clientNum, 4, 84, 84};
at::IntArrayRef testInputShape{testClientNum, 4, 84, 84};
DqnOption option(inputShape, testInputShape, deviceType);
//env
option.envNum = clientNum;
option.envStep = 1;
option.multiLifes = false;
//target model
option.targetUpdateStep = 2000;
option.tau = 1;
//prio
option.rbCap = 262144; //Cap to be 2^x, 262144
option.pbAlpha = 0.6;
option.pbBetaBegin = 0.4;
option.pbBetaEnd = 1;
option.pbEpsilon = 1e-5;
option.pbBetaPart = 0.3;
//explore
option.exploreBegin = 1;
option.exploreEnd = 0.01;
option.explorePart = 0.3;
//input
option.inputScale = 255;
option.rewardScale = 1;
option.rewardMin = -1; //TODO: reward may not require clip
option.rewardMax = 1;
option.gamma = 0.99;
//grad
option.batchSize = 32; //32
option.startStep = 10000; //1000
option.maxGradNormClip = 1;
//test
option.toTest = true;
option.testGapEp = 5000;
option.testEp = testClientNum;
option.testBatch = testClientNum;
//log
option.logInterval = 100;
option.tensorboardLogPath = "./logs/prio_testpongdqn1/tfevents.pb";
//model
option.saveThreshold = -20;
option.saveStep = 1;
option.saveModel = false;
option.savePathPrefix = "???";
option.loadModel = false;
option.loadOptimizer = false;
RawPolicy policy(option.exploreBegin, outputNum);
PrioDqn<AirCnnNet, AirEnv, RawPolicy, torch::optim::Adam> dqn(model, targetModel, env, testEnv, policy, optimizer, option);
dqn.trainDqn(epochNum);
}
void testPongDqn2(const int epochNum) {
const std::string envName = "PongNoFrameskip-v4";
const int outputNum = 6;
const int clientNum = 1;
const int testClientNum = 4;
std::string serverAddr = "tcp://127.0.0.1:10201";
LOG4CXX_DEBUG(logger, "To connect to " << serverAddr);
AirEnv env(serverAddr, envName, clientNum);
env.init();
std::string testServerAddr = "tcp://127.0.0.1:10202";
LOG4CXX_DEBUG(logger, "To connect to " << testServerAddr);
AirEnv testEnv(testServerAddr, envName, testClientNum);
testEnv.init();
LOG4CXX_INFO(logger, "Env " << envName << " ready");
AirCnnNet model(outputNum);
model.to(deviceType);
AirCnnNet targetModel(outputNum);
targetModel.to(deviceType);
// torch::optim::Adagrad optimizer(model.parameters(), torch::optim::AdagradOptions(1e-3)); //rmsprop: 0.00025
// torch::optim::RMSprop optimizer(model.parameters(), torch::optim::RMSpropOptions(0.00025).eps(0.01).alpha(0.95));
torch::optim::Adam optimizer(model.parameters(), torch::optim::AdamOptions(1e-4));
LOG4CXX_INFO(logger, "Model ready");
at::IntArrayRef inputShape{clientNum, 4, 84, 84};
at::IntArrayRef testInputShape{testClientNum, 4, 84, 84};
DqnOption option(inputShape, testInputShape, deviceType);
//env
option.envNum = clientNum;
option.envStep = 1;
option.multiLifes = false;
//target model
option.targetUpdateStep = 2000;
option.tau = 1;
//prio
option.rbCap = 262144; //Cap to be 2^x, 262144
option.pbAlpha = 0.6;
option.pbBetaBegin = 0.4;
option.pbBetaEnd = 1;
option.pbEpsilon = 1e-5;
option.pbBetaPart = 0.9;
//explore
option.exploreBegin = 1;
option.exploreEnd = 0.01;
option.explorePart = 0.3;
//input
option.inputScale = 255;
option.rewardScale = 1;
option.rewardMin = -1; //TODO: reward may not require clip
option.rewardMax = 1;
option.gamma = 0.99;
//grad
option.batchSize = 32; //32
option.startStep = 10000; //1000
option.maxGradNormClip = 1;
//test
option.toTest = true;
option.testGapEp = 5000;
option.testEp = testClientNum;
option.testBatch = testClientNum;
//log
option.logInterval = 100;
option.tensorboardLogPath = "./logs/prio_testpongdqn2/tfevents.pb";
//model
option.saveThreshold = -20;
option.saveStep = 1;
option.saveModel = false;
option.savePathPrefix = "???";
option.loadModel = false;
option.loadOptimizer = false;
RawPolicy policy(option.exploreBegin, outputNum);
PrioDqn<AirCnnNet, AirEnv, RawPolicy, torch::optim::Adam> dqn(model, targetModel, env, testEnv, policy, optimizer, option);
dqn.trainDqn(epochNum);
}
void testPongDqn3(const int epochNum) {
const std::string envName = "PongNoFrameskip-v4";
const int outputNum = 6;
const int clientNum = 1;
const int testClientNum = 4;
std::string serverAddr = "tcp://127.0.0.1:10201";
LOG4CXX_DEBUG(logger, "To connect to " << serverAddr);
AirEnv env(serverAddr, envName, clientNum);
env.init();
std::string testServerAddr = "tcp://127.0.0.1:10202";
LOG4CXX_DEBUG(logger, "To connect to " << testServerAddr);
AirEnv testEnv(testServerAddr, envName, testClientNum);
testEnv.init();
LOG4CXX_INFO(logger, "Env " << envName << " ready");
AirCnnNet model(outputNum);
model.to(deviceType);
AirCnnNet targetModel(outputNum);
targetModel.to(deviceType);
// torch::optim::Adagrad optimizer(model.parameters(), torch::optim::AdagradOptions(1e-3)); //rmsprop: 0.00025
// torch::optim::RMSprop optimizer(model.parameters(), torch::optim::RMSpropOptions(0.00025).eps(0.01).alpha(0.95));
torch::optim::Adam optimizer(model.parameters(), torch::optim::AdamOptions(1e-4));
LOG4CXX_INFO(logger, "Model ready");
at::IntArrayRef inputShape{clientNum, 4, 84, 84};
at::IntArrayRef testInputShape{testClientNum, 4, 84, 84};
DqnOption option(inputShape, testInputShape, deviceType);
//env
option.envNum = clientNum;
option.envStep = 1;
option.multiLifes = false;
//target model
option.targetUpdateStep = 2000;
option.tau = 1;
//prio
option.rbCap = 262144; //Cap to be 2^x, 262144
option.pbAlpha = 0.6;
option.pbBetaBegin = 0.4;
option.pbBetaEnd = 1;
option.pbEpsilon = 1e-5;
option.pbBetaPart = 0.9;
//explore
option.exploreBegin = 1;
option.exploreEnd = 0.01;
option.explorePart = 0.6;
//input
option.inputScale = 255;
option.rewardScale = 1;
option.rewardMin = -1; //TODO: reward may not require clip
option.rewardMax = 1;
option.gamma = 0.99;
//grad
option.batchSize = 32; //32
option.startStep = 10000; //1000
option.maxGradNormClip = 1;
//test
option.toTest = true;
option.testGapEp = 5000;
option.testEp = testClientNum;
option.testBatch = testClientNum;
//log
option.logInterval = 100;
option.tensorboardLogPath = "./logs/prio_testpongdqn3/tfevents.pb";
//model
option.saveThreshold = -20;
option.saveStep = 1;
option.saveModel = false;
option.savePathPrefix = "???";
option.loadModel = false;
option.loadOptimizer = false;
RawPolicy policy(option.exploreBegin, outputNum);
PrioDqn<AirCnnNet, AirEnv, RawPolicy, torch::optim::Adam> dqn(model, targetModel, env, testEnv, policy, optimizer, option);
dqn.trainDqn(epochNum);
}
void testBeamRider(const int epochNum) {
const std::string envName = "BeamRiderNoFrameskip-v4";
const int outputNum = 9;
const int clientNum = 1;
const int testClientNum = 4;
std::string serverAddr = "tcp://127.0.0.1:10201";
LOG4CXX_DEBUG(logger, "To connect to " << serverAddr);
AirEnv env(serverAddr, envName, clientNum);
env.init();
std::string testServerAddr = "tcp://127.0.0.1:10202";
LOG4CXX_DEBUG(logger, "To connect to " << testServerAddr);
AirEnv testEnv(testServerAddr, envName, testClientNum);
testEnv.init();
LOG4CXX_INFO(logger, "Env " << envName << " ready");
AirCnnNet model(outputNum);
model.to(deviceType);
AirCnnNet targetModel(outputNum);
targetModel.to(deviceType);
// torch::optim::Adagrad optimizer(model.parameters(), torch::optim::AdagradOptions(1e-3)); //rmsprop: 0.00025
// torch::optim::RMSprop optimizer(model.parameters(), torch::optim::RMSpropOptions(0.00025).eps(0.01).alpha(0.95));
torch::optim::Adam optimizer(model.parameters(), torch::optim::AdamOptions(3e-4));
LOG4CXX_INFO(logger, "Model ready");
at::IntArrayRef inputShape{clientNum, 4, 84, 84};
at::IntArrayRef testInputShape{testClientNum, 4, 84, 84};
DqnOption option(inputShape, testInputShape, deviceType);
//env
option.envNum = clientNum;
option.envStep = 8;
option.multiLifes = false;
//target model
option.targetUpdateStep = 2000;
option.tau = 1;
//prio
option.rbCap = 262144; //Cap to be 2^x, 262144
option.pbAlpha = 0.6;
option.pbBetaBegin = 0.4;
option.pbBetaEnd = 1;
option.pbEpsilon = 1e-5;
option.pbBetaPart = 0.9;
//explore
option.exploreBegin = 1;
option.exploreEnd = 0.05;
option.explorePart = 0.8;
//input
option.inputScale = 255;
option.rewardScale = 1;
option.rewardMin = -1; //TODO: reward may not require clip
option.rewardMax = 1;
option.gamma = 0.99;
//grad
option.batchSize = 64; //32
option.startStep = 1000; //1000
option.maxGradNormClip = 1;
//test
option.toTest = true;
option.testGapEp = 5000;
option.testEp = testClientNum;
option.testBatch = testClientNum;
//log
option.logInterval = 100;
option.tensorboardLogPath = "./logs/prio_testbeam/tfevents.pb";
//model
option.saveThreshold = -20;
option.saveStep = 1;
option.saveModel = false;
option.savePathPrefix = "???";
option.loadModel = false;
option.loadOptimizer = false;
RawPolicy policy(option.exploreBegin, outputNum);
PrioDqn<AirCnnNet, AirEnv, RawPolicy, torch::optim::Adam> dqn(model, targetModel, env, testEnv, policy, optimizer, option);
dqn.trainDouble(epochNum);
}
//TODO: increase beta
//TODO: sometimes, beta > 1 makes better performance, but beta is not encouraged to > 1
}
namespace {
void logConfigure(bool err) {
log4cxx::ConsoleAppenderPtr appender(new log4cxx::ConsoleAppender());
if (err) {
appender->setTarget(LOG4CXX_STR("System.err"));
}
log4cxx::LayoutPtr layout(new log4cxx::SimpleLayout());
appender->setLayout(layout);
log4cxx::helpers::Pool pool;
appender->activateOptions(pool);
log4cxx::Logger::getRootLogger()->addAppender(appender);
// log4cxx::Logger::getRootLogger()->setLevel(log4cxx::Level::getDebug());
log4cxx::Logger::getRootLogger()->setLevel(log4cxx::Level::getInfo());
// log4cxx::Logger::getRootLogger()->setLevel(log4cxx::Level::getError());
log4cxx::LogManager::getLoggerRepository()->setConfigured(true);
}
}
int main(int argc, char** argv) {
logConfigure(false);
// testtestCart(atoi(argv[1]));
// test103(atoi(argv[1]));
// testPong6(atoi(argv[1]));
// testBreakout(atoi(argv[1]));
// testCart(atoi(argv[1]));
// testProbe(atoi(argv[1]));
// testPongDouble2(atoi(argv[1]));
testPongDqn3(atoi(argv[1]));
// testBeamRider(atoi(argv[1]));
// testtestPong(atoi(argv[1]), argv[2]);
// testCartLog(atoi(argv[1]));
LOG4CXX_INFO(logger, "End of test");
}