SLM Lab
v4.2.3
v4.2.0v4.1.1v4.2.3
v4.2.3
v4.2.0v4.1.1v4.2.3
  • SLM Lab
  • 🖥Setup
    • Installation
    • Quick Start
  • 🚀Using SLM Lab
    • Lab Command
    • Lab Organization
    • Train: REINFORCE CartPole
    • Resume and Enjoy: REINFORCE CartPole
    • Agent Spec: DDQN+PER on LunarLander
    • Env Spec: A2C on Pong
    • GPU Usage: PPO on Pong
    • Parallelizing Training: Async SAC on Humanoid
    • Experiment and Search Spec: PPO on Breakout
    • Run Benchmark: A2C on Atari Games
    • Meta Spec: High Level Specifications
    • Post-Hoc Analysis
    • TensorBoard: Visualizing Models and Actions
    • Using SLM Lab In Your Project
  • 📈Analyzing Results
    • Data Locations
    • Graphs and Data
    • Performance Metrics
  • 🥇Benchmark Results
    • Public Benchmark Data
    • Discrete Environment Benchmark
    • Continuous Environment Benchmark
    • Atari Environment Benchmark
    • RL GIFs
  • 🔧Development
    • Modular Design
      • Algorithm Taxonomy
      • Class Inheritance: A2C > PPO
    • Algorithm
      • DQN
      • REINFORCE
      • Actor Critic
    • Memory
      • Replay
      • PrioritizedReplay
      • OnPolicyReplay
      • OnPolicyBatchReplay
    • Net
      • MLP
      • CNN
      • RNN
    • Profiling SLM Lab
  • 📖Publications and Talks
    • Book: Foundations of Deep Reinforcement Learning
    • Talks and Presentations
  • 🤓Resources
    • Deep RL Resources
    • Contributing
    • Motivation
    • Help
    • Contact
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  1. 🥇Benchmark Results

RL GIFs

A collection of replay GIFs generated from SLM Lab agents in "enjoy" mode.

PreviousAtari Environment BenchmarkNextModular Design

Last updated 3 years ago

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BeamRider

Breakout

KungFuMaster

MsPacman

Pong

Qbert

Seaquest

SpaceInvaders

Ant

HalfCheetah

Hopper

Humanoid

Inv.DoublePendulum

InvertedPendulum

Reacher

Walker

ppo pong
ppo breakout
ppo mspacman
ppo kungfumaster
ppo qbert
ppo seaquest
ppo spaceinvaders
ppo beamrider
sac doublependulum
sac ant
sac hopper
sac halfcheetah
sac reacher
sac humanoid
sac pendulum
sac walker