kandi X-RAY | DeepRL Summary
kandi X-RAY | DeepRL Summary
If you have any question or want to report a bug, please open an issue instead of emailing me directly. Modularized implementation of popular deep RL algorithms in PyTorch. Easy switch between toy tasks and challenging games. The DQN agent, as well as C51 and QR-DQN, has an asynchronous actor for data generation and an asynchronous replay buffer for transferring data to GPU. Using 1 RTX 2080 Ti and 3 threads, the DQN agent runs for 10M steps (40M frames, 2.5M gradient updates) for Breakout within 6 hours.
Top functions reviewed by kandi - BETA
- Plots ANOVA
- Load results from files
- Plot games
- Returns all log directories that match the pattern
- Run jujoco
- Merges a configuration dictionary
- Compute the loss function
- Convert input to tensor
- Perform the transition
- Greedy greedy implementation
- Get a logger
- Select the best matching parameters
- Forward function to the function
- Generate a tag
- Calculate the objective function
- Forward action function
- Records an episode
- Main loop
- Command line interface for batchatari
- Plot DDP 3D example
- Creates a random environment
- Reduce a set of patterns to a given score function
- Plots PPO
- Reduce all tfevents under the given root directory
- Compute the loss
- Run the agent
DeepRL Key Features
DeepRL Examples and Code Snippets
- **Cognitive AI** , powered by Deep Neural networks, such as - Computer vision, - Natural Language Proessing, Understanding, Generataion (NLP-NLU-NLG), - Audio & Speech analytics, - Conversational AI, - Multimodal analytics
from fast_rl.agents.dqn import create_dqn_model, dqn_learner from fast_rl.agents.dqn_models import * from fast_rl.core.agent_core import ExperienceReplay, GreedyEpsilon from fast_rl.core.data_block import MDPDataBunch from fast_rl.core.metrics impor
import gym import dm_control2gym from algorithms import PPO, TRPO, SAC, CGP, TD3 from evaluator import Evaluator from evaluator.plot import plot_learning_curves, load_dataset envs = [ ('cartpole', 'balance'), ('cartpole', 'swingup'), ('
Trending Discussions on DeepRL
In following this tutorial, I am receiving the following error:
ValueError: prefix tensor must be either a scalar or vector, but saw tensor: Tensor("Placeholder_2:0", dtype=int32)
The error originates from these lines:...
ANSWERAnswered 2017-Jul-24 at 01:52
I met the same problem with the version of tensorflow is 1.2.+.
When i changed it to 1.1.0, the problem resolved.
I think it because the API of rnn_cell.zero_state makes arg batch_size must be a scalar or vector, but not tensor.
So, if you change batch_size to scalar, e.g. 128, the problem also could be resolved.
No vulnerabilities reported
You can use DeepRL like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
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