Trying to pick up basics of reinforcement learning by self-study from some blogs and texts. Forgive me if the question is too basic and different bits that I understand are a bit messy, but even after consulting a few references, I cannot really get how Deep Q learning with a neural network works. I understood the Bellman equation like this $$V^\pi(s)= R(s,\pi(s)) + \gamma \sum_{s'} P(s'|s,\pi(s)) V^\pi(s')$$ and the update rule of Q table. $$Q_{n+1}(s_t, a_t)=Q_n(s_t, a_t)+\alpha(r+\gamma\max_{a\in\mathcal{A}}Q(s_{t+1}, a)-Q_n(s_t, a_t))$$ But …
I was trying to write reinforcement learning agent using stable-baselines3 library. The agent(abservations) method should return action. I went through different models API (like PPO) and they do not really allow us to specify action space. Instead action space is specified in environment. This notebook says: The type of action to use (discrete/continuous) will be automatically deduced from the environment action space. So, it seems that "model" deduce action space from environment. Q1. But exactly how? Q2. Also how my …
OpenAI Gym has really normalized the way reinforcement learning is performed. It makes it possible for data scientists to separate model development and environment setup/building and to focus on what they really should be focusing on. Quoting from Gym website: Background: Why Gym? (2016) Reinforcement learning (RL) is the subfield of machine learning concerned with decision making and motor control. It studies how an agent can learn how to achieve goals in a complex, uncertain environment. It’s exciting for two …
I am currently learning reinforcement learning and wanted to use it on the car racing-v0 environment. I have successfully made it using PPO algorithm and now I want to use a DQN algorithm but when I want to train the model it gives me this error: AssertionError: The algorithm only supports (<class 'gym.spaces.discrete.Discrete'>,) as action spaces but Box([-1. 0. 0.], [1. 1. 1.], (3,), float32) was provided Here is my code: import os import gym from stable_baselines3 import DQN from …
I am trying to wrap my head around the effects of is_slippery in the open.ai FrozenLake-v0 environment. From my results when is_slippery=True which is the default value it is much more difficult to solve the environment compared to when is_slippery=False. It takes roughly 10K iterations to solve when is_slippery=True compared to roughly 150 iterations when is_slippery=False. I used the same cross-entropy method for both of them. Now my issue is trying to understand the implementation from the repository and how …
I am working on a project of portfolio optimization with reinforcement learning. I would like incorporate a dependent decision process: Decide which asset should be bought. Decide about the amount which should be bought. I already found papers using this idea, but no hints regarding the implementation. I read about goal-dedicated hierarchical reinforcement learning, but it doesn't fit my needs, as no goal has to be met by the second decider. Has anybody an idea how I could implement my …
I'm trying to install packages on GOOGLE COLAB, but I'm facing Import error, I can't import Sub module of my main module 'gym'. I done the following things. First I cloned the git hub repository through git command (! git clone https://github.com/zoraiz-ali/gym.git) Then I add the directory using sys.path import sys sys.path.append('/content/gym') Code of my setup.py file is given below from setuptools import setup setup( name="gym_robot", version="0.3", url="https://github.com/zoraiz-ali/gym.git", author="Zoraiz Ali", license="MIT", packages=["gym_robot", "gym_robot.envs", "gym_robot.envs.helper", ], include_package_data=True, install_requires=["gym", "numpy", "opencv-python", "pillow"] …
The exact problem is a crate of industrial parts, made by injection molding in very high quantities. The objective is to put as much parts as possible in one crate. This is done by a small robotic arm that take the part from the injection molding machine, cold it a little and put it in the crate. The shape of the parts can be a bit complex. It can be basically anything that can be molded into a 2 parts …
I have been searching for reinforcement learning libraries or examples that aren't using a simulated environment like OpenAI Gym. I havn't been successful at all until yet and I would really appreciate if someone knows some examples/libraries. If someone is familiar with Optunas ask-and-tell procedure, thats would be exactly what I am looking for.
I am building an RL agent for which the model is defined: def build_model(states, actions): azioni = list(actions) model = Sequential() model.add(Dense(4, activation='relu', input_shape=[len(azioni)])) model.add(Dense(4, activation='relu')) return model The action.space is: self.action_space=gym.spaces.Tuple(tuple([gym.spaces.Discrete(3)]*4)) My action space consists of 4 actions for 4 agents, where agents is a list of 4 object agents. Then define Agent: def build_agent(model, actions): policy = BoltzmannQPolicy() memory = SequentialMemory(limit=50000, window_length=1) dqn = DQNAgent(model=model, memory=memory, policy=policy, nb_actions=actions, nb_steps_warmup=10, target_model_update=1e-2) return dqn When I try to build the …
I have an environment which contains 26 states. Each episode sample have an terminal state.I want my agent to learn how to get to the terminal state faster by using the minimum sequences of action. some states have obstacles. In my data simulation the actions are defined ( there are total 3 actions ). How can I prepare the environment using openAI gym ?
I'm trying to install python packages in COLAB using the following setup.py file. from setuptools import setup setup( name="gym_robot", version="0.3", url="https://github.com/zoraiz-ali/gym.git", author="Zoraiz Ali", license="MIT", packages=["gym_robot", "gym_robot.envs", "gym_robot.envs.helper", ], include_package_data=True, install_requires=["gym", "numpy", "opencv-python", "pillow"] ) I execute the following command ! python /content/gym/setup.py install This returns the following error error: package directory 'gym_robot' does not exist I do not find any solution, anyone knows how to install packages on Goole COlAB?
I am interested in creating a reinforcement learning algorithm for assigning items to different buckets, such that the buckets are almost the same weight, i.e., scale but with more than two places to distribute weight. Each item can have a certain weight, each bucket can but also doesn't need to have a maximal load, however, all buckets need to have almost the same weight in the end. For instance, if we have 5 items with weights [1,2,3,6,5] and three buckets …
I am working on training a 3 finger Jaw gripper. The environment I setup is this: UR10 3 finger robot Pybullet for Simulation Stable baselines and DDPG Observation space is RGB image stacked with Depth and Segmentation Mask Action space is dx,dy,dz added to current position of end effector (wrist of robot) alpha, beta, gamma as orientation angles of end effector and joint positions of fingers. Reward 1: (1 - ((end effector distance from object)/(some max distance)))*10 Reward 2: When …
I created my custom environment in gym, which is a maze. I use a DQN model with BoltzmannQPolicy. It trains fine with the following variables: position of the agent distance from the endpoint position of the endpoint which directions can it move to So I don't give it an image or anything. If I train and test it in the same environment (the same maze, without changing the position of walls) it can solve it easily. But if I introduce …
I've seen that OpenAI Gym environments can be registered with an optional reward threshold (reward_threshold) which represents: The reward threshold before the task is considered solved How does this value affect the learning process? Or does one have to manually compare the reward obtained in each episode with the reward_threshold and stop the learning process if it surpasses it?
I've been trying to train a DDQN to play OpenAI Gym's CartPole-v1, but found that although it starts off well and starts getting full score (500) repeatedly (at around 600 episodes in the pic below), it then seems to go off the rails and do worse the more it plays. I'm pretty new to ML so I'm not really sure what could cause this so I'm not sure how to start debugging (I've tried tweaking some of the hyper-parameters, but …
Since MADDPG uses a centralized critic for training, why not simply treat all cooperating agents as a single meta-agent with a concatenated observation space and a concatenated action space? In my opinion, MADDPG is centralized enough, so it won't hurt to go one step further.
I am making an environment using OpenAI gym for Diplomacy, and making an AI for it. In Diplomacy, a player has many units, and each unit has a number of moves available to it. Therefore, the player's action space is the product of each unit's moves, minus the combinations which make no sense. What I am doing is constructing a list of all available actions for the agent, like so: (France: FLEET Brest Coast -> English Channel, France: TROOP Marseilles …