- DeepRL-TensorFlow2 VS soft-actor-critic
- DeepRL-TensorFlow2 VS TensorFlow2.0-for-Deep-Reinforcement-Learning
- DeepRL-TensorFlow2 VS tensorforce
- DeepRL-TensorFlow2 VS ydata-synthetic
- DeepRL-TensorFlow2 VS minimalRL
- DeepRL-TensorFlow2 VS machin
- DeepRL-TensorFlow2 VS tf2multiagentrl
- DeepRL-TensorFlow2 VS Reinforcement-Learning
- DeepRL-TensorFlow2 VS Fleet-AI
- DeepRL-TensorFlow2 VS pomdp-baselines
DeepRL-TensorFlow2 Alternatives
Similar projects and alternatives to DeepRL-TensorFlow2
-
TensorFlow2.0-for-Deep-Reinforcement-Learning
TensorFlow 2.0 for Deep Reinforcement Learning. :octopus:
-
InfluxDB
Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
-
machin
Reinforcement learning library(framework) designed for PyTorch, implements DQN, DDPG, A2C, PPO, SAC, MADDPG, A3C, APEX, IMPALA ...
-
tf2multiagentrl
Clean implementation of Multi-Agent Reinforcement Learning methods (MADDPG, MATD3, MASAC, MAD4PG) in TensorFlow 2.x
-
WorkOS
The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.
-
Reinforcement-Learning
Learn Deep Reinforcement Learning in 60 days! Lectures & Code in Python. Reinforcement Learning + Deep Learning (by andri27-ts)
DeepRL-TensorFlow2 reviews and mentions
-
PPO implementation in TensorFlow2
I've been searching for a clean, good, and understandable implementation of PPO for continuous action space with TF2 witch is understandable enough for me to apply my modifications, but the closest thing that I have found is this code which seems to not work properly even on a simple gym cartpole env (discussed issues in git-hub repo suggest the same problem) so I have some doubts :). I was wondering whether you could recommend an implementation that you trust and suggest :)
-
Question about using tf.stop_gradient in separate Actor-Critic networks for A2C implementation for TF2
I have been looking at this implementation of A2C. Here the author of the code uses stop_gradient only on the critic network at L90 bur not in the actor network L61 for the continuous case. However , it is used both in actor and critic networks for the discrete case. Can someone explain me why?
Stats
marload/DeepRL-TensorFlow2 is an open source project licensed under Apache License 2.0 which is an OSI approved license.
The primary programming language of DeepRL-TensorFlow2 is Python.
Popular Comparisons
Sponsored