Unity 机器学习代理工具包 (ML-Agents) 是一个开源项目,它使游戏和模拟能够作为训练智能代理的环境。
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using System.Collections;
using System.Collections.Generic;
using UnityEngine;
/** Generic functions for parent Agent class.
* Contains all logic for Brain-Agent communication and Agent-Environment
* interaction.
*/
public abstract class Agent : MonoBehaviour
{
public Brain brain;
/**< \brief The brain that will control this agent. */
/**< Use the inspector to drag the desired brain gameObject into
* the Brain field */
public List<Camera> observations;
/**< \brief The list of the cameras the Agent uses as observations. */
/**< These cameras will be used to generate the observations */
public int maxStep;
/**< \brief The number of steps the agent takes before being done. */
/**< If set to 0, the agent can only be set to done via a script.
* If set to any positive integer, the agent will be set to done after that
* many steps each episode. */
public bool resetOnDone = true;
/**< \brief Determines the behaviour of the Agent when done.*/
/**< If true, the agent will reset when done.
* If not, the agent will remain done, and no longer take actions.*/
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public float reward;
/**< \brief Describes the reward for the given step of the agent.*/
/**< It is reset to 0 at the beginning of every step.
* Modify in AgentStep().
* Should be set to positive to reinforcement desired behavior, and
* set to a negative value to punish undesireable behavior.
* Additionally, the magnitude of the reward should not exceed 1.0 */
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public bool done;
/**< \brief Whether or not the agent is done*/
/**< Set to true when the agent has acted in some way which ends the
* episode for the given agent. */
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public float value;
/**< \brief The current value estimate of the agent */
/**< When using an External brain, you can pass value estimates to the
* agent at every step using env.Step(actions, values).
* If AgentMonitor is attached to the Agent, this value will be displayed.*/
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public float CummulativeReward;
/**< \brief Do not modify: This keeps track of the cummulative reward.*/
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public int stepCounter;
/**< \brief Do not modify: This keeps track of the number of steps taken by
* the agent each episode.*/
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public float[] agentStoredAction;
/**< \brief Do not modify: This keeps track of the last actions decided by
* the brain.*/
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public float[] memory;
/**< \brief Do not modify directly: This is used by the brain to store
* information about the previous states of the agent*/
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public int id;
/**< \brief Do not modify : This is the unique Identifier each agent
* receives at initialization. It is used by the brain to identify
* the agent.*/
void OnEnable()
{
id = gameObject.GetInstanceID();
if (brain != null)
{
brain.agents.Add(id, gameObject.GetComponent<Agent>());
agentStoredAction = new float[brain.brainParameters.actionSize];
memory = new float[brain.brainParameters.memorySize];
}
InitializeAgent();
}
void OnDisable()
{
//Remove the agent from the list of agents of the brain
brain.agents.Remove(id);
}
/// When GiveBrain is called, the agent unsubscribes from its
/// previous brain and subscribes to the one passed in argument.
/** Use this method to provide a brain to the agent via script.
* Do not modify brain directly.
@param b The Brain component the agent will subscribe to.*/
public void GiveBrain(Brain b)
{
RemoveBrain();
brain = b;
brain.agents.Add(id, gameObject.GetComponent<Agent>());
agentStoredAction = new float[brain.brainParameters.actionSize];
memory = new float[brain.brainParameters.memorySize];
}
/// When RemoveBrain is called, the agent unsubscribes from its brain.
/** Use this method to give a brain to an agent via script.
* Do not modify brain directly.
* If an agent does not have a brain, it will not update its actions.*/
public void RemoveBrain()
{
if (brain != null)
{
brain.agents.Remove(id);
}
}
/// Initialize the agent with this method
/** Must be implemented in agent-specific child class.
* This method called only once when the agent is enabled.
*/
public virtual void InitializeAgent()
{
}
/// Collect the states of the agent with this method
/** Must be implemented in agent-specific child class.
* This method called at every step and collects the state of the agent.
* The lenght of the output must be the same length as the state size field
* in the brain parameters of the brain the agent subscribes to.
* Note : The order of the elements in the state list is important.
* @returns state A list of floats corresponding to the state of the agent.
*/
public virtual List<float> CollectState()
{
List<float> state = new List<float>();
return state;
}
/// Defines agent-specific behavior at every step depending on the action.
/** Must be implemented in agent-specific child class.
* Note: If your state is discrete, you need to convert your
* state into a list of float with length 1.
* @param action The action the agent receives from the brain.
*/
public virtual void AgentStep(float[] action)
{
}
/// Defines agent-specific behaviour when done
/** Must be implemented in agent-specific child class.
* Is called when the Agent is done if ResetOneDone is false.
* The agent will remain done.
* You can use this method to remove the agent from the scene.
*/
public virtual void AgentOnDone()
{
}
/// Defines agent-specific reset logic
/** Must be implemented in agent-specific child class.
* Is called when the academy is done.
* Is called when the Agent is done if ResetOneDone is true.
*/
public virtual void AgentReset()
{
}
/// Do not modify : Is used by the brain to reset the agent.
public void Reset()
{
memory = new float[brain.brainParameters.memorySize];
CummulativeReward = 0f;
stepCounter = 0;
AgentReset();
}
/// Do not modify : Is used by the brain to collect rewards.
public float CollectReward()
{
return reward;
}
/// Do not modify : Is used by the brain to collect done.
public bool CollectDone()
{
return done;
}
/// Do not modify : Is used by the brain give new action to the agent.
public void UpdateAction(float[] a)
{
agentStoredAction = a;
}
/// Do not modify : Is used by the brain to make the agent perform a step.
public void Step()
{
AgentStep(agentStoredAction);
stepCounter += 1;
CummulativeReward += reward;
if ((stepCounter > maxStep) && (maxStep > 0))
{
done = true;
}
}
/// Do not modify : Is used by the brain to reset the Reward.
public void ResetReward()
{
reward = 0;
}
}